Accurate classification of lncRNAs and mRNAs is important for the identification of tissue- or disease-specific lncRNAs. Here, we present our tool LncDC (Long non-coding RNA detection) that is able to accurately predict lncRNAs with an XGBoost model using features extracted from RNA sequences, secondary structures, and translated proteins. Benchmarking experiments showed that LncDC consistently outperformed six state-of-the-art tools in distinguishing lncRNAs from mRNAs. Notably, the use of sequence and secondary structure (SASS) k-mer score features and flexible ORF features improved the classification capability of LncDC. We anticipate that LncDC will definitely promote the discovery of more and novel disease-specific lncRNAs. LncDC is implemented in Python and freely available at  Similar to messenger RNAs (mRNAs), lncRNAs are typically transcribed by RNA polymerase II so that most of them have 5’-end caps, 3’-end poly(A) tails, alternative splicing, and are subjected to other post-transcriptional processes2. However, compared with mRNAs, lncRNAs are usually less evolutionarily conserved and have lower expression levels, making them difficult to be identified accurately3. In addition, lncRNAs are expressed more tissue- or condition-specific than mRNAs indicating that there are still abundant lncRNAs to be discovered in different tissues and with different conditions4.Advancement in next-generation sequencing (NGS) technology, especially transcriptome profiling, enabled the discovery of thousands of novel RNA transcripts. Among them, a class of RNA transcripts named long non-coding RNAs (lncRNAs) obtained special attention. lncRNAs are RNA transcripts longer than 200 nucleotides (nt) without protein-coding capabilities5. Also, lncRNAs can bind proteins to form lncRNA-protein complexes, leading to signaling pathways alternation or mRNA splicing modification5. The complicated functions of lncRNAs and their broad involvement in different biological processes make them a research hotspot in biomedicine.Even though lncRNAs do not code for proteins, they are involved in diverse biological processes that regulate the expression of protein-coding genes, such as chromosome modification, transcription machinery interfering, gene-dosage compensation, and enhancer-associated activation9. In particular, lncRNAs become new diagnostic biomarkers and potential therapeutic targets for cancer treatment because of their aberrant expression in different cancer cells. For instance, the prostate cancer-specific lncRNA gene prostate cancer antigen 3 (PCA3) is overexpressed in prostate cancer cells but can barely be found in other cancers. As an effective diagnostic biomarker for prostate cancer, highly expressed PCA3 can be detected from urine samples of prostate cancer patients10. Although researchers have already identified many previously unannotated lncRNAs, some of which are cancer-specific, more disease-specific lncRNAs are yet to be identified.lncRNAs are involved in diverse disease conditions such as neurodegenerative diseases, cardiovascular diseases, and cancer12. Although evidence from RT-PCR and MS is reliable, such validation is labor-intensive and time-consuming when many lncRNA candidates need to be identified. Besides wet-lab approaches, alignment-based homology search in protein databases of multiple species is also used to identify lncRNAs, such as coding-potential calculator (CPC)13. However, alignment-based methods are adept in evaluating protein coding potential for interspecific or intraspecific conserved RNA transcripts such as mRNAs but not suitable for lncRNAs because they are mainly species-, tissue-, and/or condition-specific. For example, the GENCODE project v7 catalog of human lncRNA shows that near 30% of the identified lncRNA transcripts are primate-specific11. In addition, lncRNAs that are partially overlapped with protein-coding genes can easily be misclassified as mRNAs through alignment-based homology search methods15.RNA-Seq is commonly used to identify novel lncRNA transcripts because of its high throughput characteristic and sensitivity in detecting lowly expressed RNA transcripts. Wet-lab approaches reverse transcription polymerase chain reaction (RT-PCR) and mass spectrometry (MS) of peptides are always required for the validation of newly discovered lncRNAs14. If an RNA has a long ORF, it usually means that it has a high probability of being translated into a protein, even though studies suggest that some small ORFs can encode micro peptides of less than 100 amino acids in length16. Fickett score is a linguistic feature used to measure the variety of nucleotide positions and compositions between mRNAs and lncRNAs17. Hexamer usage bias is a measurement that distinguishes mRNAs and lncRNAs based on the fact that a hexamer in the longest ORF of a mRNA can decide the adjacent amino acids in the translated peptide18. PLEK applies a support vector machine (SVM) model with an improved k-mer scheme to predict lncRNAs and mRNAs, where the k-mer patterns indicate a specific string order with k nucleotides in an RNA transcript. CPC2 is an upgraded version of CPC, which integrated a SVM model with Fickett score, ORF length, ORF integrity, and protein pH isoelectric point (PI) features to identify lncRNAs19. Besides the features mentioned ahead, CPPred proposed a feature combination named CTD features, which describe the nucleotide composition and distribution in an RNA transcript20. LncFinder is an R package that applies ORF, secondary structure, and physicochemical property-based features to identify lncRNAs21. COME integrates sequence-derived and experiment-based features for lncRNA identification, such as GC content, sequence conservation, expression abundance and histone modification state22.Several alignment-free methods or tools have been developed to overcome the flaws of the alignment-based approaches. Alignment-free methods mainly extract sequence-derived features from mRNAs and lncRNAs and apply different machine learning algorithms to distinguish them. CPAT differentiates mRNAs and lncRNAs by using a logistic regression model with several features, including open reading frame (ORF) length, ORF coverage, Fickett score, and hexamer usage bias23. Due to the difference between the longest ORF and CDS, these mRNAs are easily misclassified as lncRNAs. Second, RNA transcripts are capable of forming specific secondary structures. Those secondary structures contribute to the functions of RNA transcripts, such as transcription and translation factors targeting, scaffolding for RNA–protein complexes in nuclei, or interfering with post-translational modification of proteins in cytoplasm25. The secondary structures that mRNAs and lncRNAs formed can be different, but few tools use secondary structure-based information to aid classification of the two types of RNA. Third, most of the current tools apply a random under-sampling strategy for balancing training data, which randomly eliminates several majority class examples. For instance, lncFinder randomly selects 8,000 lncRNAs and 8,000 mRNAs from the GENCODE database to construct the training dataset of human, although the number of mRNAs in this database far exceeds the number of lncRNAs21. Random under-sampling is easy to perform, but some data that may have an important impact on classification may be lost, thereby reducing the performance of model prediction26.Although current machine learning-based tools have encouraging performances in lncRNA identification, they still have some drawbacks. First, most of them heavily rely on the longest ORF of an RNA transcript. However, in the gold-standard NCBI RefSeq manually curated gene annotations, there are 1909 mRNA transcripts whose coding sequence (CDS) differ from the longest ORF, indicating that features extracted from the longest ORF do not always accurately represent the properties of CDS27. We also designed novel secondary structure features that integrate both primary sequence and secondary structure information from RNA transcripts. For each RNA transcript, we translated it to a predicted protein and extracted PFs from the protein sequence. We used recursive feature elimination with cross-validation (RFECV) for feature selection, which prunes a single feature in each step and stops until the performance of model prediction does not drop any more28. During model training, we used synthetic minority over-sampling technique (SMOTE) to reduce loss of potentially valuable data by introducing new minority class samples between adjacent minority class examples29. We benchmarked the performance of LncDC against six existing tools , and the results showed that LncDC outperformed these state-of-the-art tools on both human and mouse testing datasets. LncDC allows researchers to train customized models with their own data, with or without secondary structure features. LncDC can also automatically balance training data so that researchers can use datasets with large differences in the number of mRNAs and lncRNAs to train models. LncDC is mainly used for the identification of lncRNAs in humans, mice, and other closely related species.Here, we developed an alignment-free, machine learning-based tool named LncDC, which integrates sequence intrinsic features (SIFs), secondary structure features (SSFs), and protein features (PFs) with the XGBoost algorithm to detect lncRNAs from RNA-Seq data. In order to fully capture the characteristics of CDS, we defined four types of ORF similar to those described in FEELnc30. Metastasis is the primary cause of OS-associated death, and the 5 year survival rate of OS patients diagnosed with metastasis is less than 20%31. At present, the diagnosis of OS relies on X-ray, computed tomography (CT) scan, magnetic resonance imaging (MRI), and biopsy, and the treatment of OS includes radiation, surgery, and chemotherapy32. Human epidermal growth factor receptor 2 (HER2) as a biomarker was proposed to be a treatment target because of its abnormal expression in OS cancer cells and association with lung metastasis, but the drug targeted to HER2 didn’t improve the survival rate of OS patients significantly34. In contrast to protein biomarkers, recent studies show that lncRNAs become promising biomarkers or therapeutic targets for OS, such as fibroblast growth factor receptor 3 antisense transcript 1 (FGFR3-AS1), hypoxia-inducible factor-2α promoter upstream transcript (HIF2PUT), and taurine upregulated gene 1 (TUG1)35. For instance, FGFR3-AS1 is upregulated in OS cancer cells and correlated with metastasis and poor prognosis. Knockdown of FGR3-AS1 inhibits both the proliferation of OS cells in vitro and the growth of xenograft tumors in vivo36. Overexpression of HIF2PUT decreases the growth rate, migration, and sphere-forming ability of OS cancer cells possibly due to its negative regulation to transcription factor hypoxia-inducible factor-2α (HIF2α)37. To discover more novel lncRNA biomarkers or therapeutic targets, we developed a bioinformatics pipeline that combines LncDC and other popular bioinformatics tools to detect novel OS-specific lncRNAs from 180 OS RNA-Seq data obtained from the therapeutically applicable research to generate effective treatments (TARGET) database (https://ocg.cancer.gov/programs/target). In total, we identified 97 novel OS-specific lncRNA transcripts in OS tissues by this pipeline, which were previously unannotated and were not detected in adjacent normal bone tissues. These newly identified lncRNAs with high confidence expand the annotation of OS transcriptome and provide potential biomarkers or therapeutic targets for OS treatment.Osteosarcoma (OS) is the most common primary malignancy that starts in bones. It usually occurs in children and adolescents, while individuals older than 60 also have a high occurrence rateTo overcome the drawback that the longest, conventionally defined ORF of many mRNAs is different from their annotated CDS, we defined four types of ORFs  k-mer scores to capture the relationship between an RNA primary sequence and its secondary structure. SASS k-mer scores were concordantly higher in mRNAs but lower in lncRNAs within H-Train Fig. . The patWe first compared performances of different models with a total of 57 extracted features. We used H-Train to train models with distinctive algorithms, including logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), and XGBoost. For each model, a tenfold cross-validation was applied for parameter tuning. The performances of different models on the human test dataset (H-Test) are displayed in Table As shown in Table At first, we examined the feature importance by gain, which represents the relative contribution of a feature for predicting binary categories by the XGBoost model. As shown in Fig. Next, we evaluated tenfold cross-validation accuracy scores of different feature subsets on H-Train to select the best feature subset. The cross-validation accuracy scores of various feature subsets are shown in Fig. Among the 28 features selected  was evaluated on H-Test. It showed that the overall performance of the model was dropped once the 6 selected SSFs were removed . The very high accuracy score indicated that LncDC had a powerful discriminative capability in classifying lncRNAs and mRNAs in H-Test. We also investigated the performance of LncDC when SSFs were excluded, and the result showed that the sensitivity score was slightly increased to 0.9822, but the accuracy, precision, and MCC scores were dropped to 0.9783, 0.9746, and 0.9567, respectively, indicating that SSFs were important to the overall performance of LncDC especially in precision. Surprisingly, LncDC had the highest MCC score than other tools, which is about 8% higher than PLEK and 4% higher than LncFinder.Moreover, we evaluated the performances of different tools on the mouse test dataset (M-Test) Table . The accFor LncDC, we trained a model with M-Train, named it as LncDC mouse model, and then evaluated its performance on M-Test. The result showed that the LncDC mouse model achieved the best performance with an accuracy of 0.9758 and a MCC score of 0.9516. We also investigated the performance of the LncDC mouse model when SSFs were removed. It showed that the overall performance of the LncDC mouse model without SSFs was slightly worse than the LncDC mouse model; especially, the precision was decreased from 0.9809 to 0.9751.Next, we were curious about if the LncDC human model is robust or not in mammalian lncRNA prediction, such as mice. We evaluated the performance of the LncDC human model on M-Test, and the result showed that the human model also had an excellent performance. Although compared to the LncDC mouse model, the accuracy and MCC scores of the LncDC human model were dropped from 0.9758 and 0.9516 to 0.9723 and 0.9446, respectively, it still outperformed other tools. The performance of the LncDC human model without SSFs was also evaluated. Compared to the LncDC human model with SSFs, the human model without SSFs had slightly higher sensitivity but lower specificity and precision.After genome mapping and assembling, a total of 704,690 and 721,219 assembled RNA transcripts were obtained from Route 1 and Route 2 approaches of our bioinformatics pipeline, respectively  in some literatures because of the intergenic location.The newly identified lncRNA transcripts with ‘X’ or ‘O’ codes overlapped with known genes, such as protein-coding genes and pseudogenes. For instance, the lncRNA transcript located on chr11:106,620,650—106,782,318 overlaps with a protein-coding gene GUCY1A2 on the opposite strand, implying that it might be an antisense lncRNA. The GUCY1A2 gene was reported to be upregulated in gastric cancer tissues and is associated with a poor prognosisWith widespread applications of sequencing techniques in cancer research, a large amount of RNA-Seq data has been generated and accessible to researchers. In addition to the measurement of gene expression, those RNA-Seq data are also treasures for discovering novel RNAs, especially cancer-specific RNAs. lncRNA, as a large category of RNAs, plays a considerable role in various biological processes, such as gene regulation and chromosome modification. Due to somatic mutation in cancer genomes, some lncRNAs are possibly dysregulated and highly expressed in a cancer-specific manner. Those newly discovered cancer-specific lncRNAs will potentially contribute to cancer diagnosis and therapy.27. On the contrary in LncDC we extracted max ORF length, hexamer score, and relative codon bias features from the additional defined ORFs, and those features displayed two distinguished clusters for mRNAs and lncRNAs in H-Train although they were in different patterns comparing to the conventionally defined ORF  on both H-Test and M-Test, indicating that LncDC has a powerful ability in distinguishing lncRNAs and mRNAs. LncDC is implemented in Python and enables multi-thread running, allowing fast model training and lncRNA prediction. Our pipeline identified 97 novel, high-quality lncRNAs from OS cancer transcriptome data and expanded the annotations of OS transcriptome, providing potential diagnostic biomarkers or targets for OS therapy.41. Considering that lncRNAs are longer than 200 nt and the time cost of secondary structure prediction by RNAfold increased a lot when the RNA tends to be very long, we filtered out transcripts with a length of < 200 or > 20,000 nt, besides removing transcripts with invalid nucleotide symbols such as ‘N’ and ‘S’42. After such filtrations, there are 105,163 RNA transcripts left in the human dataset for downstream analysis, including 48,165 lncRNAs and 56,998 mRNAs   and 48,479 human lncRNA transcripts from the GENCODE database (human release 34) to build a human dataset with a total of 105,595 transcripts41. After data filtration with the same strategy as in human data, we got 56,443 transcripts in the constructed mouse dataset, which included 18,603 lncRNAs and 37,840 mRNAs  and 18,856 mouse lncRNA transcripts from GENCODE (mouse release M25) to build a mouse dataset44.To identify novel OS-specific lncRNAs in OS tumor tissues, we downloaded 180 RNA-Seq data of 93 OS patients from the TARGET-OS project. The OS tumor tissues were collected from the primary solid tumors of the patients, followed by paired-end sequencing on the Illumina Genome Analyzer IIx platform (~ 80 million reads per sample on average). The OS RNA-Seq data are available by the accession number ‘phs000468’ in the dbGaP database, and the relevant NCBI BioProject accession number is PRJNA89527. To identify OS-specific lncRNAs, we need to make sure that these lncRNAs can only be detected in OS tumors rather than adjacent normal bone tissues that can be used as control in sampling. Unfortunately, the TARGET-OS project does not have paired RNA-Seq data from normal bone tissues. Thus, we downloaded 9 paired-end RNA-Seq data of 3 adjacent normal bone tissue samples  as normal controls from the gene expression omnibus (GEO) database The framework of LncDC design and implementation is shown in Fig. 45.Lt denotes the length of an RNA transcript. In general, mRNAs are longer than lncRNAs. A large portion (~ 40%) of the annotated human lncRNAs only have two exons, while most mRNAs have more than two exons11.GC content: The frequency of guanine (G) and cytosine (C) in an RNA transcript. Protein coding sequences usually have higher GC content than noncoding sequences, such as 5’ untranslated regions (UTRs) and introns, because they have fewer stop codons in which adenine (A) and thymine (T) are enriched27. As shown in Supplementary Fig Max ORF length: ORF is a subsequence of an RNA transcript that is likely to be the protein-coding region of the RNA. A long ORF indicates that the RNA transcript has a high possibility to be translated into a protein. mRNAs usually have longer ORFs than lncRNAs. Considering that the CDS and the longest ORF sequence are different in many annotated mRNA transcripts, we adopted features derived from various types of ORF defined in FEELnc to capture the CDS informationMax ORF coverage: The length of the max ORF divided by the length of the RNA transcript.Average ORF length: The average length of all T0 ORFs.Average ORF coverage: The average length of all T0 ORFs divided by the length of the RNA transcript.17. It computes four position values and four content values followed by a weighted summation. The position value is used to represent which codon position a nucleotide prefers. The position values of different nucleotides in an RNA transcript are calculated as follows:14.Fickett score: The Fickett score integrates the nucleotide composition and codon usage bias to classify coding and non-coding RNAs46. To calculate the RCB value of an ORF, the codon usage bias of codon triplets needs to be figured out and then multiplied together. The codon usage bias (Relative codon bias (RCB): The RCB was used to measure the codon triplet usage bias for the ORFs of an RNA transcriptThe RCB value of an RNA transcript ORF is calculated as follows:14.Hexamer score: A hexamer in an RNA transcript is a subsequence of any six adjacent nucleotides. Since a codon includes three adjacent nucleotides, a hexamer can represent the dependence between two continuous codons in an RNA transcript or two amino acids in a putative protein. We made a hexamer probability lookup table for the LncDC human model by calculating the hexamer probabilities of 34,539 lncRNAs and the CDS of 37,698 mRNAs in H-Train. Because each nucleotide in a hexamer can be A, T, G, or C, there are a total of 4,096  hexamers in the hexamer probability table. The table for the LncDC mouse model was constructed with the M-Train dataset in a similar way to the human lookup table used. For a specific hexamer(X), the coding and non-coding probabilities are calculated as follows:Hexamer score of ORFs: The hexamer score of different types of ORFs. ORF is the potential coding region of an RNA transcript. Both mRNAs and lncRNAs have ORFs, although the ORFs of mRNAs are generally longer than those in lncRNAs. This feature represents the diverse hexamer usage between the ORFs in mRNAs and lncRNAs.42. RNAfold uses dynamic programming to predict the secondary structure of an RNA transcript with the minimum free energy (MFE)47. The assumption is that when free energy becomes lower, the secondary structure of an RNA transcript becomes more stable. In RNAfold, the secondary structure of an RNA transcript is divided into different types of substructures, including hairpin loop, interior loop, bulge loop, multi loop, and stacking pairs. The free energy values of the substructures are different, and the free energy of an RNA secondary structure is the summation of the free energy of these substructures. Finally, the most stable secondary structure of each RNA transcript is generated by RNAfold and used for downstream feature extraction.In LncDC, the RNAfold program is used for secondary structure prediction of RNA transcripts10.40.MFE: MFE reveals the stability of a secondary structure. Lower MFE usually represents a more stable secondary structure. Evidence shows that mRNAs are more durable than lncRNAs, suggesting that they tend to have lower MFEs than lncRNAs11.Paired ratio: Given the secondary structure of an RNA transcript, we will be able to know which nucleotides have Watson–Crick pairings and which nucleotides are unpaired. If an RNA transcript has a high paired ratio, its secondary structure will be more stable.12.Number of various loop structures: The number of different types of loops in an RNA transcript, containing hairpin loop (N(H)), interior loop (N(I)), bulge loop (N(B)), and multibranch loop (N(M)). The typical loop structures are shown in Supplementary Fig 13.Coverage of various loop structures: The ratio of the number of different loops divided by the length of an RNA transcript.14.GC content of paired nucleotides: In the secondary structure of a specific RNA transcript, some of the nucleotides are paired with each other while others are not. This feature measures the GC content of those paired nucleotides. The G-C bond is stronger and more stable than the A-T bond. If an RNA transcript has more G-C pairs, its secondary structure will be more durable.15.Sequence and secondary structure (SASS) k-mer scores: We combined a primary sequence and the secondary structure of an RNA transcript to build a two-row array, as shown in Fig. We created five SASS k-mer tables to store the counts of the SASS k-mers. Since there are four distinctive nucleotides, ‘A’, ‘U’, ‘G’ and ‘C’ in the primary sequence and three structure forms ‘.’, ‘’ in the secondary structure of the RNA, the total number of combinations will be different among the SASS k-mer tables. The number of distinctive SASS k-mers from SASS 1-mer to SASS 5-mer are 12 , 144 , 1,728 , 20,736  and 248,832 , respectively. For a specific SASS k-mer(X), the mRNA and lncRNA probabilities are calculated as follows:48.In a cell, mRNAs will be translated to proteins, but lncRNAs are not. However, lncRNAs also have ORFs as mRNAs have, even though their ORFs are usually shorter. We assume that if the longest ORF of a lncRNA transcript can be translated to a protein, this protein will be different from mRNA proteins in many aspects. In LncDC, we used the ProtParam tool from the BioPython package to extract protein features from RNA transcripts16.49.pH isoelectric point (PI): the theoretical pH isoelectric point of a protein. The PI of a protein is the pH value when the protein’s net charge is zero, calculated through the acid dissociation constant (pK) values of amino acids17.Molecular weight (MW): the molecular weight of a protein in Daltons.18.50.Grand Average of Hydropathy (GRAVY): the hydropathicity of distinctive amino acids is different. The GRAVY value of a specific protein is calculated by summing up the hydropathy values of all the amino acids that the protein contains and being divided by protein length in amino acids19.51.Aromaticity: the amino acids phenylalanine (Phe), tryptophan (Trp), and tyrosine (Tyr) have an aromatic ring structure inside. The aromaticity value of a particular protein is the summation of the numbers of Phe, Trp, and Tyr and then divided by the protein length in amino acids20.52.Instability index (II): The instability index represents the stability of a protein in a test tube. It is calculated by adding up the weighted instability value of each dipeptide where the weights are decided based on the frequency that a dipeptide occurs in experimentally identified unstable and stable proteins54.In this study, various machine learning algorithms were compared, including logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), and XGBoost. The XGBoost algorithm is implemented by the xgboost package while others are implemented by the scikit-learn package of python55. LR is easy to implement and simple for interpretation. However, LR constructs a linear decision boundary so that it is not appropriate for non-linear problems. One solution for this limitation is to increase the number of features such that the dataset can be separated linearly in a high-dimensional feature space.LR fits a logistic curve to a particular dataset, representing the probability of an instance belonging to a specific category, in this case, mRNA or lncRNA56. In this project, the CART algorithm is used for DT construction. Although DT has many advantages, it is easy to overfit to the dataset, therefore ensemble models which give the prediction based on several DTs were proposed, such as RF and XGBoost57.DT is a tree model that splits the data by several decisions. It has a root node, branches, and leaves. Each path from the root to a leaf defines a rule for the classification of mRNAs and lncRNAs. DT is also easy to interpret and can handle linearly inseparable data. Because DT is a non-parametric model, its prediction does not assume that the data have a specific distribution. Various DT algorithms have been proposed, such as ID3, C4.5, and classification and regression trees (CART). The trees that the CART algorithm builds are binary, suggesting that each node in the tree has two edges55. Compared to DT, RF usually has better performance and less overfit to the dataset.As an improvement of DT, a method named bagging constructs many DTs with a bootstrap approach and gives the prediction based on the majority vote of all trees. RF is a special case of bagging that only a subset of features is considered at each tree split53. To avoid overfitting, XGBoost applies a regularization function to penalize the complexity of the model. Similar to RF, XGBoost considers a subset of features during the tree splitting process as well. XGBoost has been used in biomedical fields for years and achieves good performances in predicting the function or category of biomolecules58.XGBoost is a boosting approach that aggregates several CARTs, where the trees are constructed sequentially. The final prediction value of a specific instance is the summation of the predictions in each tree55. It allows non-linear boundaries when using different kernels, such as polynomial kernel and radial kernel, which measure the similarity between two instances. SVM is robust to high-dimensional data, but its training speed is slower than other algorithms when a large number of samples are analyzed57.SVM is an approach that uses a hyperplane to separate categories  where the minimal distance from the instances to the hyperplane is maximizedFor each algorithm, several models with distinctive hyperparameters were trained and a tenfold cross-validation approach was used for hyperparameter tuning in H-Train. Different models were evaluated in H-Test, and at last, the XGBoost model was selected and used in LncDC because its overall performance was better than others.54. All the 57 extracted features were used to fit a model on H-Train, and the average accuracy score with a tenfold cross-validation was calculated. The features were ranked by their importance scores obtained from the model, followed by eliminating the features with the lowest score. The rest of the features were used to fit a new model and a new average accuracy score was calculated.It was possible that several extracted features were redundant or did not have discriminative classification ability than others. Here, we applied the scikit learn module—recursive feature elimination with cross-validation (RFECV) to select the most powerful features for differentiating mRNAs and lncRNAs55. For instance, a change from 500 to 600 (100 increase) on the ORF length feature is thought to be a more significant shift than the change of a GC content from 0.4 to 0.6 (0.2 gain) by a classifier, while the latter one may have more contribution for differentiating lncRNA and mRNA in reality. To prevent this situation, we standardized the datasets by z-score: z = (x-μ)/σ, where x is the original value in datasets, μ is the mean value of each feature, and σ stands for the standard deviation of each feature. Theoretically, we cannot know the mean value and standard deviation of test data, so we used the mean and standard deviation of the train datasets to standardize the feature values in test datasets.In model training, features with a large magnitude may have more impact on the classifiers than the features with a small scaleData balancing is also critical for machine learning. The model trained from an unbalanced dataset might lead to biased predictions towards the majority class. For instance, if 99% of the RNAs in the dataset are lncRNAs and the classifier simply predicts every RNA is lncRNA, we would see that the classifier can classify 99% of the RNAs correctly, which is heavily misleading. Since the test datasets were already balanced, we would balance the training datasets during the model training process.27. However, when removing majority class instances, a piece of potentially critical information contributing to classifier learning has also been eliminated.The randomly under-sampling strategy for data balancing was used in previously developed lncRNA identification tools, such as FEELnc and CPAT, which randomly dropped a proportion of majority class instances to keep the same instance number as the minority class29. The steps of the SMOTE approach are as follows: (1) decide how many new minority instances are required, (2) calculate the k nearest neighbors of each minority instance in the feature space, (3) randomly select a minority instance x, (4) based on the number of instances required for oversampling, randomly select a certain number of nearest neighbors of x, (5) for each x-neighbor pair, randomly select a point along the line segment between x and its neighbor as the new synthesized minority instance, and (6) repeat the process until the amount of minority class and majority class is balanced.To avoid the drawback of the randomly under-sampling strategy, we applied the minority over-sampling strategy, SMOTE, to balance training datasets. Instead of oversampling the minority class with replacement, the SMOTE approach synthesizes new minority instances along with the lines that connect a minority instance and its nearest neighborsThe performances of LncDC and other tools tested were evaluated by the following standard performance metrics:27.Accuracy evaluates how many instances are correctly classified. Although it is a common metric for evaluating a binary classification model, it cannot comprehensively assess if the model is good or not, especially in an unbalanced test dataset. Sensitivity reflects how many true lncRNAs are picked up by the tool. In contrast, specificity evaluates how many true mRNAs are detected. Precision assesses how many detected lncRNAs are true lncRNAs. F-score and MCC were used to measure the global performance of the tool. F-score is the harmonic mean of precision and sensitivity, and MCC gives a balanced measure even if the number of instances belonging to two classes is different in the test dataset59. If we compare various classifiers, the one with the highest AUC is believed to be the best classifier.In addition, ROC curves were used to evaluate the performances of different classifiers. The ROC curve represents the relationship between true positive rate (TPR) and false positive rate (FPR). Generally, a good classifier has a curve above the TPR = FPR diagonal line and tends to be on the top left corner61. In our pipeline, we applied two series of widely used approaches (Route 1 and Route 2) for genome mapping and transcript assembling to obtain accurate and consistent RNA transcript assemblies. The pipeline for transcripts reconstruction is shown in Supplementary Fig 63. We also employed Cuffmerge to merge all of the transcript assemblies to integrated files for the OS and normal control RNA-Seq data, respectively. In Route 2, we used HISAT2 v2.2.0 for reads mapping and StringTie v2.1.2 for assembly reconstruction64. To ensure the reconstructed transcript assemblies were expressed in OS or normal tissues, only the assemblies with expression levels in fragments per kilobase million (FPKM) larger than one were selected for downstream analysis.180 RNA-seq data collected from 93 tumor samples of OS patients and 9 RNA-Seq data obtained from 3 adjacent normal bone tissue samples were used to conduct transcript reconstruction. Raw data were quality-controlled and cleaned by FastQC v0.11.9 and TrimGalore v0.6.565. Only the transcript assemblies longer than 200 nt with Cuffcompare generated class codes of ‘U’, ‘I’, ‘O’, and ‘X’ were selected as lncRNA candidates. ‘U’ stands for unknown intergenic transcripts, ‘I’ indicates that the transcripts fall entirely within a reference intron, ‘O’ stands for generic exonic overlap with reference transcripts, and ‘X’ stands for exonic overlap with reference transcripts on the opposite strand (Supplementary Fig 11. We also used the CD-HIT program to filter out transcript assemblies that exist in the NONCODE Human database and the transcripts from normal control with 80% identity66. We conducted filtration with the NONCODE database because it contains lncRNAs obtained from literature mining but may not be verified by GENCODE or NCBI RefSeq yet.The expressed RNA transcript assemblies were compared with known human genome annotations by Cuffcompare, such as ENSEMBL and NCBI RefSeq67. We removed those transcripts assemblies that only have a single exon to avoid having a large number of false positives in the candidates. Moreover, only the candidate transcript assemblies with the same positions along chromosomes obtained by both Route 1 and Route 2 were kept as high-confidence candidates and fed to LncDC for downstream identification of OS-specific novel lncRNAs.Cufflinks and StringTie were reported to reconstruct many transcript assemblies with a single exon, and most of them are false positivesSupplementary Information.


Recent technological and conceptual advances will allow the elucidation of the physiological, ecological, and chemical contributions of understudied marine fungi to coral holobiont and reef ecosystem functioning and health and may help provide an outlook for reef management actions.Tropical coral reefs are hotspots of marine productivity, owing to the association of reef-building corals with endosymbiotic algae and metabolically diverse bacterial communities. However, the functional importance of fungi, well-known for their contribution to shaping terrestrial ecosystems and global nutrient cycles, remains underexplored on coral reefs. We here conceptualize how fungal functional traits may have facilitated the spread, diversification, and ecological adaptation of marine fungi on coral reefs. We propose that functions of reef-associated fungi may be diverse and go beyond their hitherto described roles of pathogens and bioeroders, including but not limited to reef-scale biogeochemical cycles and the structuring of coral-associated and environmental microbiomes  Conceptual perspective on known and proposed functions of understudied marine fungi on coral reefs and in reef-building corals, their main ecosystem engineers. Tropical coral reefs are highly diverse and productive ecosystems shaped by their main ecosystem engineers, reef-building corals. Corals are “holobionts,” multicellular animal hosts associated with a diverse suite of prokaryotes and microeukaryotes . We further discuss the potential spectrum of interactions of fungi with other organisms on and around reefs, ranging from mutualism and commensalism to parasitism. Based on these comparisons, we hypothesize that fungi may play a pivotal role in the health and ecological functioning of coral reefs, and in reef-building coral holobionts in particular. Finally, we conclude our work with future research directions that we hope will stimulate the advancement of research of fungi on coral reefs.Fungi in the marine realm, and on coral reefs in particular are understudied compared to terrestrial and freshwater ecosystems , respectively, as well as by prokaryotes and fungi , Hyalorhinocladiella , and Physalospora spp. . Within the Eurotiomycetes, notable representatives are Aspergillus spp. or Penicillium spp. .Few studies have investigated the community dynamics of coral-associated fungal communities in response to environmental change. While it appears that coral-associated fungal communities might be host-specific  is considered a potential barrier to fungal growth  associated with Pacific yew trees , which feed into specialized biosynthetic pathways involving large multimodular enzymes such as polyketide synthases PKSs, nonribosomal peptide synthetase NRPSs, prenyltransferases, and terpene cyclases  and the use of genome mining approaches to identify BGCs has tremendously increased our knowledge in recent years  in the unicellular parasitic Microsporidia to around 2 Gb in Pucciniales (rust fungi), in the same order of magnitude as the human genome Stajich . Fungal via hybridization occurs sexually or asexually . The observed gene duplications may reflect the requirements of a more demanding environment, such as a marine habitat, which selects for the retention of duplicated genes even when resulting changes in encoded protein activities are very slight . This in turn affects the fitness and capabilities of fungal cells after germination, stress and antifungal resistance, mycotoxin and secondary metabolite production, and virulence  during certain stages in the fungal life cycle may produce phenotypic variation in response to fluctuating or changing environments, which may be conducive to survival and acclimation. Conidiation, the formation of conidiophores from vegetative hyphae, is one such critical stage. Wang et al.  reportedFinally, little is known about host–fungi symbioses and their underlying molecular mechanisms of symbiosis establishment and maintenance on coral reefs. Major changes in host–symbiont gene (co-) expression reflecting genetic reprogramming and modulation of molecular crosstalk may facilitate novel associations, as reported for arbuscular mycorrhizal fungi symbioses . Thereby, marine fungi may help mobilize organic C in the ocean via the remineralization of recalcitrant high molecular weight detritus, thereby diverting energy to higher trophic levels through saprobic , and sulfur (S) in marine systems  or potentially even the tissues organic S compounds (including sulfides and methanethiol) are readily metabolized by different marine fungal isolates, suggesting a tentative contribution to coral reef S cycling .In conclusion, fungi may contribute to coral reef biogeochemical cycling, albeit at likely varying spatial scales and levels of biological organization Figs  and 4. Evia the generation of more accessible, intermediate decomposition products of recalcitrant organic matter, which bacteria (or other organisms) cannot access on their own  as QS molecules capable of regulating the production of antimicrobial metabolites  regulate morphological differentiation and mycotoxin production , a concerted, density-dependent cell-to-cell signaling process, is one of the most widely studied chemical communication strategies in fungi Hogan . UnicellAspergillus-infested beetle larvae, which in turn is used by parasitoid wasps as a cue to detect unfavorable environments  are ubiquitous in a wide range of organisms, play major roles in biological processes, e.g. regulating inflammation and cellular homeostasis in metazoans. Oxylipins are major mediators of cross-kingdom talk and host–fungal interactions that encompass predator–prey, mutualistic, and pathogenic and biological processes  are small compounds that diffuse easily through water and gas and play a critical role in biosphere–atmosphere interactions, in plant signaling, and as infochemicals in multitrophic interactions  highly depends upon these complex multipartite interactions  of affected tissues and gall formation. Affected tissue samples contained high loads of septate fungal hyphae, and culturing efforts identified the putative agent of the disease, a soil-dwelling saprobic fungus affiliated to A. sydowii demonstrated a correlation of the secondary metabolites sydowinol, sydowinin A and B, and sydowic acid with strain pathogenicity  were laden with fungal hyphae following a marine heat wave resulting in “coral bleaching.” Opportunistic coral-associated saprobic fungi may be able to overcome weakened immune defenses of their vulnerable host, analogous to human secondary fungal infection in the aftermath of viral disease , as observed in phytoplankton–chytrid pathosystems  have yet to be characterized, it may be possible they divert photosynthate from coral-associated algae  parasitic fungi in coral reef food webs.One of the best studied examples of aquatic fungal interactions are parasitic associations of chytrids with phytoplankton  yet to be discovered. The lack of examples of such mutualistic associations of marine fungi with other biota may be due to a bias toward the study of fungal pathogens and opportunists, a trend noticeable also in the study of terrestrial crop and human pathogen emergence.via fungal chemical communication and defense, microbiome structuring as well as biogeochemical cycling, thereby extending beyond their previously reported roles as pathogens and opportunists. In the following, we briefly summarize major challenges and open questions in the study of reef-associated fungi and propose a multidisciplinary toolbox to help address these questions.Taken together, we anticipate that functions of reef-associated fungi are likely diverse, spanning a spectrum of interkingdom interactions that may mediate processes at different levels of biological organization, from cell–cell interactions to ecosystem-scale effects. These may be facilitated vice versa, how do fungal interactions on coral reefs shape their (a)biotic environment from the cellular to the ecosystem level?Diverse knowledge gaps remain regarding the ecology of marine fungi in coral reef environments. Broadly, outstanding questions include: how does the taxonomic and functional diversity of fungal communities in corals and on coral reefs distribute across space and time; which ecological guilds and types of (symbiotic) interactions do occur in coral holobionts and on the reef; what are the mechanistic underpinnings of microbe–microbe and host–microbe interactions that fungi engage with in coral holobionts and on coral reefs; which (a)biotic drivers govern, maintain, and alter fungal communities and interactions; These questions could be addressed by employing a multipronged approach which targets different levels of biological organization for an integrated view of fungal functions in complex holobionts and the ecosystem Fig. :SymPortal, which resolves “defining” intragenomic variants for ITS2-type profiles of coral-associated endosymbiotic algal communities  is challenged by e.g. the pervasive amount of host-derived nucleic acids, which constitute a major hurdle for culture-independent sequencing approaches, as well as well-known limits to microbial cultivability  prior to unlabeled coculture, and subsequently preserved and prepared for correlative electron microscopy and SIMS analysis to visualize (sub)cellular assimilation and distribution of isotopic labels and metabolic interactions between cells. Such approaches, especially in combination with correlative fluorescence in situ hybridization  and omics applications as outlined above, would allow for the assessment of interactions in natural microbe–microbe assemblages or in clearly defined, synthetic communities of reduced complexity by targeting specific microbial functional groups or combinations of taxa. Notably, cultures of major coral-associated algal symbionts are available at dedicated microbial culture collections and can be readily maintained in stable cultures. Similarly, the availability of well-characterized coral bacterial isolates is steadily increasing, facilitating functional studies of microbe–microbe interactions in vitro  could be addressed in a combination of different culture-dependent and -independent applications. High-throughput OSMAC applications on microbial cocultures , as detailed for the study of complex multipartite host–microbe interactions , ex situ , or in situ  experiments, as recently established for rate measurements of selected prokaryotic or holobiont-level metabolisms, such as dinitrogen fixation or oxygen fluxes . Such approaches may hold the promise to address the work outlined above in a) to c), to help elucidate fungal contributions to reef-scale biogeochemical cycling.Finally, assessing the roles that fungi play at the community and ecosystem-levels and their spatio-temporal dynamics will be critical to advance the knowledge of fungal functions in coral reef ecosystems. Understanding and predicting how changes in coral reef fungal communities might translate into community-scale cascade effects and shifts should become a major goal. These include but are not limited to the (a) determination of (a)biotic drivers of fungal distribution and abundance in space and time, (b) quantification of fungi-specific activities at the community level such as their involvement in nutrient and energy transfer within and between the benthic and the pelagic reef environment, and (c) modeling of community-level budgets (e.g. C or N budgets) as well as ecological networks under consideration of fungal activity. In terrestrial environments, climate has been often reported as one of the strongest drivers affecting fungal community composition  from simpler ones, which requires energy, whereas the latter is the breakdown of complex molecules into simpler ones (i.e. catabolites) releasing energy.The former describes a cumulative effect, i.e. less than additive, i.e. less than the sum of effects . The latter defines a cumulative effect greater than the additive sum of effects.Aspergillus. These fungi can infect a wide range of hosts, ranging from coral to humans.A disease caused by fungi of the genus Processes that connect the sea floor (i.e. the benthic zone) and the water column (i.e. the pelagic zone) through the exchange of energy, mass, or nutrients. It plays a prominent role in nutrient cycling and energy transfer in aquatic food webs, and thereby ecosystem processes. BGC: tightly linked sets of (mostly) nonhomologous genes participating in a common, discrete metabolic pathway. The genes are arranged in physical proximity to each other on the genome, and their expression is often coregulated. Common in bacterial and fungal genomes and most widely known for the production of secondary metabolites.Carbohydrate-active enzymes which build and break down complex carbohydrates and glycoconjugates for a large body of biological roles (collectively studied under the term of Glycobiology).Movement of a cell or organism in response to an environmental diffusible chemical substance.Unicellular or mycelic, aerobic zoosporic fungi that operate as saprotrophs and pathogens in freshwater, brackish, and marine habitats.A phylum within the animal kingdom which includes jellyfish, anemones, and reef-building corals. Cnidaria are simple, multicellular organisms characterized by two main cell layers (ectodermis and endodermis) and an apparatus consisting of stinging cells (cnidocytes) for prey capture and defense.The ability of several fungi to switch between a multicellular hyphal and unicellular yeast morphology and growth form. The mechanism underlying this biological reorganization process depends on external  triggers.via the DMSP demethylation or DMSP cleavage pathways.An organosulfur compound produced in vast quantities by phytoplankton and seaweeds, and known to have osmoprotectant and antioxidant function. DMSP is an important carbon source for many marine bacteria, which can break it down Any group of species that exploit the same resources, or that exploit different resources in related ways. Among fungi, common guilds are decomposers (saprobic), pathogens, endophytes, and mycorrhiza.A group of organisms including cyanobacteria, fungi, algae, and bacteria that dwell in the pore space of rocks and similar substrates, such as coral skeletons.A molecular technique that uses fluorescent probes that bind to only particular parts of a nucleic acid (DNA or RNA) sequence with a high degree of sequence complementarity. Widely used in the field of microbial ecology to identify taxa and to visualize the distribution and proportion of specific taxa within environmental samples.Matrix Assisted Laser Desorption Ionization coupled to Time-Of-Flight mass spectrometry.The observed shift in ecosystem trophic structure toward higher microbial biomass and energy use. On coral reefs, causes of microbialization include overfishing and eutrophication.Refers to the behavior, precise control, and manipulation of fluids geometrically constrained to a small scale. In (micro)biology, it offers a powerful approach to control the complete cellular environment, thereby enabling the study of microbial community microscale organization, cellular behavior, adaptation, or gene expression.Mycorrhizae are soil-borne fungi closely associated with the roots of terrestrial plants. Arbuscular mycorrhiza colonize the intercellular spaces of plant roots . Arbuscular mycorrhizae are considered vital endosymbionts of plant holobionts, as they enhance productivity.Sequencing is the process of determining the order of nucleotides in entire genomes or targeted regions of DNA or RNA. Next-generation sequencing (NGS) is a technology that offers ultrahigh throughput, scalability, and speed, and includes applications such as metabarcoding (deep sequencing of target regions) for whole community studies, whole genome sequencing, RNA sequencing, or the assessment of genome-wide DNA methylation and DNA–protein interactions.The former is the imbalance between the systemic manifestation of reactive oxygen species (ROS) and a biological system's ability to readily detoxify the reactive intermediates or to repair the damage resulting in cellular components, including proteins, lipids, and the DNA. Oxidative stress responses encompass the production of antioxidant enzymes , which aim to strike a balance between ROS production and consumption.Lipids, often bioactive, generated by the oxidation of polyunsaturated fatty acids (PUFAs).Compounds with a high vapor pressure and low water solubility. Although VOCs gas can be emitted from different solids and liquids, in this manuscript we only refer to VOCs of biogenic origin (i.e. produced by living organisms).Study of all the VOCs that are produced by a biological matrix (organism and ecosystem).One Strain MAny Compounds is an approach, which by altering cultivation parameters , aims to activate silent BGCs and expand or modify the metabolite production fingerprints of microbial strains.Live microorganisms with beneficial qualities for a host/recipient organism. Probiotic microorganisms help to restore recipient health by antagonistic action against pathogenic microbes, or enhance performance and growth by providing nutritional benefits to the recipient.Describes the bioavailability for or timescales of degradation of organic matter by organisms, which is reflected in the timescales by which this matter is respired. Organic matter follows a spectrum of recalcitrant  to labile .A technique used to analyze the composition of solid surfaces and thin films, which permits the spatial mapping of atoms or molecules. In correlation with electron microscopy increasingly used in biological research to create isotopic maps of histological sections, which can be used to visualize the assimilation and translocation of nutrients within complex symbiotic systems, such as corals.15N2 can be used to find out which microbes are active nitrogen fixers.A technique in microbial ecology for tracing uptake of nutrients by microbes. A substrate is enriched with a heavier stable isotope, i.e. consumed by the organisms to be studied. Biomarkers with the heavier isotopes incorporated into them can be separated from biomarkers containing the more naturally abundant lighter isotope by buoyant density centrifugation. As an example, Process of making an RNA copy (mRNA) of a gene's DNA.fuac028_Supplemental_FileClick here for additional data file.


GP2 expression was induced on CD103+CD11b+ cDC within the SI‐LP and was regulated by IRF4, TGFβR1‐ and retinoic acid signalling. Mice selectively lacking Gp2 on CD103+CD11b+ cDC (huLang‐Cre.gp2fl/fl mice) had normal numbers and proportions of innate and adaptive immune cells in the SI‐LP suggesting that GP2 expression by CD103+CD11b+ cDC is not required for intestinal immune homoeostasis.The intestinal lamina propria (LP) contains distinct subsets of classical dendritic cells (cDC), each playing key non‐redundant roles in intestinal immune homeostasis. Here, we show that glycoprotein 2 (GP2), a GPI‐anchored protein and receptor for bacterial type‐I fimbriae, is selectively expressed by CD103 Within the intestine, cDC are found diffusely distributed within the intestinal lamina propria (LP). They are also present within gut‐associated lymphoid tissues (GALT), including the Peyer's patches and isolated lymphoid follicles. Within the LP, cDC scan the tissue for foreign and self‐antigen and subsequently migrate via lymphatics to intestinal‐draining mesenteric lymph nodes (MLN) where they present antigen to T cells. The context of this interaction is essential in determining the outcome of the ensuing adaptive immune response.+CD11b− cDC, cross‐present epithelial derived antigen to CD8+ T cells and are critical for cross‐tolerance as well as intestinal Th1 and intra‐epithelial lymphocyte (IEL) homeostasis.+CD11b+ and CD103−CD11b+ cDC, are essential for intestinal Th2 responses, and intestinal Th17 homeostasis.+α4β7+ gut tropic T cells.Murine cDC consist of two major cDC subsets; cDC1 and cDC2 that each play non‐redundant roles in intestinal immune homeostasis. Intestinal cDC1, identified by their expression of XCR1, or as CD103Escherichia coli into the intestinal mucosa and to be protective in DSS‐induced colitis.Glycoprotein 2 (GP2) is a 78 kDa glycosylphosphatidylinositol (GPI)‐anchored membrane protein expressed by several epithelial glandular tissues, acinar cells of the pancreas and microfold (M‐) cells in the follicular‐associated epithelium (FAE) of Peyer's patches (PP).Gp2 transcripts are enriched in SI‐LP CD103+CD11b+ cDC.+CD11b+ cDC2 and their migratory equivalents in draining MLN, while being largely absent from other immune cells in the intestine. GP2 expression on CD103+CD11b+ cDC2 was regulated by IRF4 and by environmental cues, including both TGFβ and retinoic acid. Finally, we found that mice whose CD103+CD11b+ cDC2 lack gp2 have normal numbers and proportions of innate and adaptive immune cells in the SI‐LP, indicating that its expression by cDC2 is not required for intestinal immune homeostasis.Transcriptional profiling of murine intestinal cDC has suggested that 22.1Itgax‐Cre,Irf4tm1Rdf/J,dnRARlsl/lsl,human‐Langerin‐Cre ,MyD88floxGp2flox mice.The following mouse strains were used in the course of this study: 2.2Genotyping was performed on mouse ear biopsies using the following oligonucleotides: GP2 KO, flox and wt mice; GP2_FW 5′ CATCAACAAAACGGGACTCATA3′, GP2_RV1 5′ CCTAGAAGGGACTACACTGG3′, GP2_RV2 5′ TTTTTAAAGGAATGAAAGGCTGT3′. hu‐Langerin Cre mice; iCRE_FW 5′ GCACCTGGGCCAGCTCAACAT3′, iCRE_RV 5′ TGGTCAAAGTCAGTGCGTTCA3′. IRF4 flox and wt mice; IRF4_FW 5′ CCACTCTCTGCTTCCCTGTC3′ and IRF4_RV 5′ CTCTCGACCAATTCCTCAAAGT3′. To discriminate IRF4 flox and delta mice; IRF4_FW1 5′ AGCTTGCCGTAGGTGGCATCG3′, IRF4_FW2 5′ CCACTCTCTGCTTCCCTGTC3′ and IRF4_RV 5′ CTTCCTCATCTCCGGGCCTTTCG3′. RARdn flox and wt mice; ROSA1 (common) 5′ AAAGTCGCTCTGAGTTGTTAT3′, ROSA2 5′ GCGAAGAGTTTGTCCTCAACC3′ and ROSA3 (wt) 5′ GGAGCGGGAGAAATGGATATG3′.2.32, and cells collected from the culture after 20 hours. For isolation of MLN and splenic cells, organs were mashed through a 70 μm cell strainer, except in Figure For isolation of LP cells, single‐cell suspensions were generated as previously described.2.4The following antibodies were used for flow cytometry: NK1.1 (PK 136), CD19 (6D5 or 1D3), CD3e (17A2 or 145‐2C11), MHC II(IA/I‐E) (M5/114.15.2), CD11b (M1/70), CD8α (53‐6.7), Ly6C (HK1.4 or AL‐21), CD45.2 (104), B220 (RA3‐6B2), CD45.1 (A20), TCRβ (H57‐597), CD8β (53‐5.8 or YST156.7.7 or eBioH35‐17.2), CD4 (RM4‐5 or GK1.5 or eBioGK1.5), Ter119 (TER‐119), IFN‐γ (XMG 1.2), Siglec H (551), CD11c (N418), IL17A (TC11‐18‐10.1), FoxP3 (FJK‐165), CD103 (M290), Siglec F (E50‐2440), CD64, (X54‐5/7.1), CD135 (A2F10), CD117 (2B8), CD45.2 (104) and XCR1 (ZET) all from eBioscience, BioLegend or BD Biosciences. Antibody to GP2 (2F11 C3) was from MBL.Flow cytometry was performed according to standard procedures.2.5+Itgax‐Cre.Tgfbr1fl/fl.Rag1−/− or Tgfbr1fl/fl.Rag1−/− BM+ or CD45.1+CD45.2+ C57Bl/6 BM at a 1:1 ratio (2 × 106 cells/mouse) into irradiated (900 rad) congenic CD45.1+CD45.2+ or CD45.1+ C57Bl/6 recipients, respectively. Recipients received ciprofloxacin  in their drinking water for 3 weeks, and analysis was performed 8 weeks after BM reconstitution.Mixed bone marrow (BM) chimeras were generated by i.v. injection of CD45.22.6C57BL/6 mice received R848  by oral gavage or were injected i.p. with FliC  or polyI:C . MLN were isolated 17 hours after treatment and analysed by flow cytometry.2.74 cells/well) were incubated in R10 medium, in the presence or absence of LPS  or FliC  for 22 hours at 37°C and 5% CO2. Cells were analysed by flow cytometry, and levels of IL‐6 in cell supernatants assessed using the cytometric bead array (CBA) flex set kit (BD Bioscience) according to manufacturer's instructions.SI‐LP, LI‐LP and MLN cell suspensions were re‐stimulated in vitro with PMA  and Ionomycin  for 4 hours. At 1 hour of re‐stimulation, cells were treated with Brefeldin A . For cDC cultures, FACS‐sorted cDC (2 × 102.8t test except where indicated and GraphPad Prism software (GraphPad).Statistical significance was determined with Student's 33.1+ cells, and by few cDC derived from the large intestinal (LI) LP, lung or spleen  and cDC2 (CD103+CD11b+ and CD103−CD11b+)  Figure . GP2 was2 Figure ; howevere Figure . GP2 wasC Figure . In the 1 Figure C,D, sugge Figure E. In cons Figure G, indica3.2hi intestinal‐derived CD103+CD11b+ cDC2 and was not expressed by MLN‐resident cDC1 or cDC2  or pI:C (TLR3 ligand), or orally with R848 (TLR7 ligand), and accumulation of GP2+CD103+CD11b+ cDC in the MLN assessed 17 hours later. All three TLR ligands elicited an accumulation of both GP2+CD103+CD11b+ and GP2−CD103+CD11b+, cDC2 into the MLN  have an almost complete loss of intestinal‐derived CD103+CD11b+ cDC in MLN and an approximate 50% reduction in SI‐LP CD103+CD11b+ cDC.+ and GP2− SI‐LP CD103+CD11b+ cDC , is an important regulator of intestinal homeostasis and has been implicated in intestinal cDC differentiation.s Figure A, indicae Figure B. Thus, 3.5+CD11b+ cDCs.+ SI‐LP cDC2, BM from CD45.1+ WT and either CD45.2+Itgax‐Cre.Tgfbr1fl/fl.Rag1−/− (Cre+) or control CD45.2+Tgfβr1fl/fl.Rag1−/− (Cre−) was injected at a 1:1 ratio into irradiated CD45.1+CD45.2+ recipients and the SI‐LP cDC compartment of recipient mice assessed 8 weeks post‐transfer. Consistent with previous findings,+CD11b+ cDC deriving from Itgax‐Cre.Tgfbr1fl/fl.Rag1−/− BM had reduced levels of CD103 , monocytes (CD64+CD11b+Ly6C++MHCII−), monocyte intermediates (CD64+CD11b+Ly6C+MHCII+) and neutrophils as Gp2fl/fl littermates (Figure +CD11b+ cDC is not required for intestinal immune homeostasis.To determine whether absence of GP2 on intestinal CD103P Figure . Similars Figure , CD4+ ans Figure  as well s Figure D. Regards Figure , the lat4+CD11b+ cDC2 express the zymogen granule membrane protein GP2. GP2 was not expressed on pre‐DC, cDC2 in the lung, spleen or LI‐LP suggesting local induction in the SI‐LP. Consistent with this, we demonstrate a local and direct role for the vitamin A metabolite, RA, in inducing GP2 expression on small intestinal CD103+CD11b+ cDC2 in vivo. In contrast, cell‐intrinsic absence of TGFβ signalling resulted in enhanced GP2 expression on CD103+CD11b+ cDC2 and upregulation of GP2 on additional SI‐LP and LI‐LP cDC2 populations. SI‐LP‐derived GP2+CD103+CD11b+ cDC2 were present in the MLN, and while GP2 expression on these cells was independent of Myd88 signalling, migration of GP2+CD103+CD11b+ cDC2 to the MLN was dramatically enhanced following administration of TLR agonists. Finally, mice whose CD103+CD11b+ cDC2 were deficient in GP2 had normal immune cell composition and numbers in LI‐LP, SI‐LP and MLN suggesting that cDC2‐derived GP2 is not required for maintaining intestinal homeostasis.In the current study, we demonstrate that a large proportion of SI‐LP CD103Irf4 is required for the survival of SI‐LP CD103+CD11b+ cDC2; its absence leading to an approximate 50% reduction in these cells.+CD11b+ cDC population but not GP2−CD103+CD11b+ cDC are reduced in the absence of Irf4. A likely explanation for these results is that IRF4, in addition to promoting CD103+CD11b+ cDC2 survival, is required for the induction of GP2 on these cells, increasing the numbers of GP2−CD103+CD11b+ cDC2 in Itgax‐Cre.Irf4fl/fl mice. Alternatively, GP2+CD103+CD11b+ cDC2 as a population may be more dependent on IRF4 for their survival compared with their GP2‐negative counterparts. Future studies comparing the transcriptome of GP2− and GP2+CD103+CD11b+ cDC may help resolve these possibilities.We have previously shown that +CD11b+ LI‐LP cDC2 strongly indicates that GP2 is induced on CD103+CD11b+ cDC2 by factors within the SI‐LP environment. Here, we identify a role for retinoic acid signalling in cDC in inducing GP2 expression in SI‐LP CD103+CD11b+ cDC2. Of note, we have previously shown retinol levels to be higher in the SI compared with the LI and that SI‐LP cDC receive increased RA signals in the steady state compared with their counterparts in the LI‐LP.+CD11b+ cDC2. Why GP2 is not expressed by closely related CD103−CD11b+ cDC2, or by CD103+CD11b− cDC1 in the SI‐LP remains unclear. One potential explanation is that these populations receive reduced RA signalling in situ compared with CD103+CD11b+ cDC2; however, we think this unlikely since SI‐LP CD103+CD11b‐ cDC1 are themselves a major source of RA.The absence of GP2 on cDC precursors, on extra‐intestinal cDC2 and expression of GP2 on an only minor fraction of CD103+CD11b+ cDC2 from CD103−CD11b+ cDC2 intermediates.+CD11b+ cDC2. Surprisingly, while SI‐LP CD103+CD11b+ cDC2 showed an expected reduction in CD103 expression, the proportion of GP2 expressing CD103+CD11b+ cDC2 as well as the levels of GP2 expression on these cells increased in the absence of TGFβR signalling. Moreover, while in the absence of TGFβR signalling SI‐LP and LI‐LP CD103+CD11b− cDC remained GP2−, a proportion of SI‐LP CD103−CD11b+ cDC2 expressed GP2, as did colon CD103+CD11b+ and CD103−CD11b+ cDC2, albeit at lower levels compared with their corresponding populations in the SI‐LP. Thus, TGFβR signalling acts to curtail GP2 expression on cDC2 in both the SI‐LP and LI‐LP. Why intestinal CD103−CD11b+ cDC2, whose expression of GP2 appears to be suppressed in vivo by TGFβ, do not upregulate CD103 in response to such signals remains unclear. Interestingly, despite our finding the GP2 is expressed by a subset of splenic cDC1, and thus not restricted to a specific cDC subset, absence of TGFβR signalling failed to induce GP2 expression on SI‐LP and LI‐LP cDC1. Collectively, our findings suggest that GP2 expression by cDC is regulated by a complex interplay of local environmental factors.Cell intrinsic TGFβR signalling has been suggested to play a key role in the development of SI‐LP CD103+CD11b+ cDC in the MLN, and these cells increase in number following in vivo TLR stimulation indicates that cDC‐derived GP‐2 may play a role in the regulation of intestinal adaptive immune responses. Consistent with this possibility, GP2 has been implicated in modulating T cell responses.Our observation that GP2 is expressed on a subset of SI‐LP‐derived CD103+CD11b+ cDC and that expression of GP2 by intestinal cDC is regulated by local environmental signals including RA and TGFβ. Further studies are required to address whether GP2+ and GP2− CD103+CD11b+ cDC represent developmentally and functionally distinct cDC subsets, or different activation stages of the same subset as well as the importance of CD103+CD11b+ cDC‐derived GP2 in intestinal infection and inflammation.In summary, we demonstrate that GP2 is expressed by a large population of SI‐LP CD103WWA and KML conceived and designed the study. KML, CDS, FA and KK performed experiments and analysed the data. HO and AM provided key reagents. KML and WWA wrote the manuscript with input from all authors.The authors declare no commercial or financial conflict of interest.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.


BEAM方案：卡莫司汀300 mg/m−2·d−1，第4、5天；环磷酰胺50 mg·kg−1·d−1，第6、7天。2. Bu/Cy-E方案：白消安1.6 mg/kg，每12 h 1次，第1～3天；依托泊苷400 mg·m2，第1天；依托泊苷100 mg·m−2·d−1，第2～5天；阿糖胞苷100 mg/m2，每12 h 1次，第2～5天；环磷酰胺1～1.5 g·m−2·d−1，第2～5天；克拉屈滨6 mg·m−2·d−1，第2～5天。3. BEAC方案±克拉屈滨：司莫司汀300 mg/m2，第1天；塞替派5 mg/kg，每12 h 1次，第2、3天。4. 卡莫司汀+塞替派方案：卡莫司汀400 mg/m四、定义9/L连续3 d；血小板植入：PLT>20×109/L连续7 d且脱离血小板输注。4级中性粒细胞缺乏：ANC<0.5×109/L；3级中性粒细胞缺乏：ANC（0.5～1.0）×109/L；中性粒细胞缺乏伴发热：ANC<1.0×109/L伴体温>38.0 °C；4级血小板减少：PLT<25×109/L；3级血小板减少：PLT（25～50）×109/L；4级贫血：HGB<65 g/L。感染：由于部分中性粒细胞缺乏患者无法明确致病原，需要根据体格检查、实验室及影像学检查判断或经验性抗菌药物治疗有效中性粒细胞植入：ANC>0.5×10五、随访随访资料来自门诊/住院病历或电话随访记录。主要随访内容为是否发生感染发热、出血，有无疾病复发、死亡及原因。随访截止日期为2021年12月31日。PEG-rhG-CSF组、rhG-CSF组中位随访时间分别为152（26～323）d、805（340～2058）d。六、统计学处理t检验比较组间临床差异和移植后造血重建情况。采用Kaplan-Meier法计算中位血小板或中性粒细胞植入时间，Log-rank检验进行组间中性粒细胞植入时间、血小板植入时间、生存率及复发率比较并绘制恢复曲线图。以P<0.05为差异有统计学意义。应用SPSS 26.0软件包进行数据分析。采用卡方检验和一、临床参数比较+细胞量方面差异均无统计学意义（P>0.05）。患者基线临床参数详见PEG-rhG-CSF组75例，rhG-CSF组74例，两组患者除预处理方案外，在年龄、性别构成、IPI评分、ECOG评分、放疗比例、移植前疾病状态、体重以及回输CD34二、造血重建比较P＝0.010，P＝0.030，P＝0.007）。两组粒细胞缺乏伴发热发生率差异无统计学意义（84.0％对82.4％，P＝0.798），但PEG-rhG-CSF组粒细胞缺乏伴发热的持续时间短于rhG-CSF组（4.0 d对5.5 d，P＝0.005），3级血小板减少持续中位时间也短于rhG-CSF组（5.0 d对6.5 d，P＝0.016）。其余评价指标（包括血小板植入时间、4级血小板减少持续时间、移植后1个月内单采血小板输注量、4级贫血发生率、4级贫血持续时间、移植后1个月内红细胞输注量）组间差异均无统计学意义（P<0.001）。PEG-rhG-CSF组4级粒细胞缺乏持续中位时间为9.0（5～16）d，3级粒细胞缺乏持续中位时间为2.0（0～7）d，中性粒细胞植入中位时间为9.5（7～17）d，与rhG-CSF组相比差异均有统计学意义（三、感染发生情况P＝0.247），血流感染发生率分别为5.3％（4/75）、9.5％（7/74）（P＝0.336）。感染大多位于口腔黏膜、上呼吸道、肺部和消化道。PEG-rhG-CSF组、rhG-CSF组感染发生率分别为22.7％（17/75）、31.1％（23/74）（四、生存与复发P＝0.638），OS率分别为100.0％、（98.6±1.3）％（P＝0.312）。截止到2021年12月31日，PEG-rhG-CSF组中2例患者于移植后6个月内复发，无死亡病例；rhG-CSF组2例患者于移植后6个月内复发，6例患者于移植1年后复发，1例患者于移植后217 d死亡。PEG-rhG-CSF组、rhG-CSF组移植后6个月DFS率分别为（96.4±2.5）％、（94.7％±2.6）％（五、不良反应P＝0.048），但疼痛持续时间短暂且未发生硬结及感染。其他PEG-rhG-CSF/rhG-CSF相关过敏反应、神经系统症状、消化道症状、泌尿系统症状、凝血功能异常在两组中均未发生。两组主要不良反应均为骨骼及肌肉疼痛，主要部位为腰背部及季肋区，其中PEG-rhG-CSF组6例（8.00％），rhG-CSF组5例（10.8％），口服非甾体抗炎药（NSAID）均获得缓解。与PEG-rhG-CSF组相比，rhG-CSF组注射部位疼痛发生率较高（1.30％对8.70％，auto-HSCT可以延长复发/难治淋巴瘤患者的无病生存时间，但移植后严重的中性粒细胞减少及其并发症是治疗相关死亡的主要原因以往研究显示NHL患者auto-HSCT前采用BEAM或Bu/Cy-E预处理方案均耐受性良好，具有相似的毒性特征和生存结果，造血恢复时间无明显差异，获益/风险比相当聚乙二醇（PEG）没有毒性和免疫原性，可通过肾小球安全滤过清除，而经过PEG化蛋白修饰的rhG-CSF，其血浆半衰期延长至47 h，同时药物溶解性增加、生物利用度提高、制剂稳定性增强在Martino等的随机对照研究中，MM患者接受大剂量美法仑预处理auto-HSCT，PEG-rhG-CSF组与rhG-CSF组在中性粒细胞和血小板植入时间方面没有显著差异，但PEG-rhG-CSF组中性粒细胞减少伴发热的发生率和持续时间明显降低，安全性和有效性与rhG-CSF组相当Aaron等根据既往研究结果可知，移植后第1天或第6天起给予PEG-rhG-CSF，至少在促进中性粒细胞植入、降低粒细胞缺乏伴发热发生率等方面的效果不低于rhG-CSF，对于是否缩短住院时间及减少费用方面尚存在争议，且大部分研究中多发性骨髓瘤患者的比例较高，淋巴瘤患者占比相对较低，大剂量美法仑为主要预处理方案。本研究结果显示，淋巴瘤患者auto-HSCT后第1天起使用PEG-rhG-CSF 100 µg/kg每周1次能促进中性粒细胞植入，缩短粒细胞缺乏伴发热持续时间，效果优于rhG-CSF。但不能降低感染发生的风险。PEG-rhG-CSF虽然对auto-HSCT后4级血小板减少的天数无明显影响，但能减少3级血小板减少的持续时间，可能与骨髓微环境的调节有关，使得血小板的恢复更加稳定。PEG-rhG-CSF对红系造血的影响不明显，严重贫血的发生率和持续时间在两组间不存在统计学意义差异。除此以外，PEG-rhG-CSF的使用能缩短移植住院天数，降低住院费用。注射PEG-rhG-CSF后最常见骨骼及肌肉酸痛、乏力，个别可见皮疹、发热、流涕等感冒样症状，其他的不良反应包括头晕、注射部位疼痛等，一般反应均为1/2级，不需特殊处理，停药后即自行消失，较严重的不良反应包括严重变态反应、急性呼吸窘迫综合征等发生率较低综上，本研究结果显示淋巴瘤患者auto-HSCT后应用PEG-rhG-CSF在促进粒系造血恢复、减少粒细胞缺乏伴发热等方面优于rhG-CSF，耐受性良好，移植后6个月DFS率及OS率未观察到明显差异。PEG-rhG-CSF给药次数明显减少，可减少患者痛苦并减轻临床工作人员负担，值得临床推广应用。


Without relying on cultivation, metagenomic sequencing greatly accelerated the novel RNA virus detection. However, it is not trivial to accurately identify RNA viral contigs from a mixture of species. The low content of RNA viruses in metagenomic data requires a highly specific detector, while new RNA viruses can exhibit high genetic diversity, posing a challenge for alignment-based tools. In this work, we developed VirBot, a simple yet effective RNA virus identification tool based on the protein families and the corresponding adaptive score cutoffs. We benchmarked it with seven popular tools for virus identification on both simulated and real sequencing data. VirBot shows its high specificity in metagenomic datasets and superior sensitivity in detecting novel RNA viruses.https://github.com/GreyGuoweiChen/RNA_virus_detectorBioinformatics online. RNA viruses have high diversity and can infect a large variety of eukaryotes from planktons to humans. With an estimated number of over 100 million eukaryotic viruses, <0.1% RNA viruses are recorded . HoweverIn this work, we introduce VirBot, an easy-to-use yet effective RNA virus detection tool for metagenomic data. It takes assembled contigs as input and detects ones from RNA viruses. In addition, it outputs the taxonomic labels for the detected RNA viruses. We validated VirBot in various scenarios and benchmark it with other RNA virus detection tools. In samples that only contain novel RNA viruses, VirBot achieves higher recall than other tools. And VirBot also demonstrates high specificity in metagenomic data that only contains a small number of RNA viruses.VirBot detects RNA viruses using our self-constructed protein domain families and their customized score cutoffs. As shown by a recent analysis . FinallyWe validated VirBot in five scenarios and compared its performance against seven popular tools. The commands and parameters of the tools are summarized in Simulated metagenomic data. We first evaluated VirBot on two semi-synthetic marine metagenomic samples (NCBI SRA ID: ERR1992810 and ERR2185279) constructed from 82 eukaryotes, 365 prokaryotes and DNA/RNA viruses (Identifying new RNA virus. To assess the ability of VirBot in identifying new RNA viruses, we used three datasets that contain newly sequenced RNA viruses. One comprises 8849 RNA phages that were barely detected before, while eukaryotic viruses dominate known RNA viruses. The second dataset is an RNA virome sample sequenced from marine water containing 114 139 RNA viral sequences. The third is made up of 5504 non-redundant sequences containing viral RdRp region detected from the global ocean resources. As shown in Real metagenomic data. We tested VirBot on 21 human clinical samples for pathogen detection. VirBot successfully retrieved all the reported RNA viral pathogens and achieved an average recall of 99.3% and precision of 99.2% among different groups. The result is shown in In this work, we constructed a comprehensive protein family database for RNA viruses and derived the adaptive bit score cutoff using a large number of non-RNA viral proteins. We developed VirBot based on these RNA viral pHMMs for fast and more sensitive RNA virus detection in metagenomic data. Our pHMMs are designed specifically for RNA virus and show higher sensitivity than other HMM-based tools . Comparebtad093_Supplementary_DataClick here for additional data file.


C.elegans, previous screens using Caenorhabditis elegans were strongly biased to uncover members of this pathway in the maintenance process but not in the initiation process. To identify novel piRNA pathway members, we have utilized a sensitized reporter strain which detects defects in initiation, amplification, or regulation of piRNA silencing. Using our reporter, we have identified Integrator complex subunits, nuclear pore components, protein import components, and pre-mRNA splicing factors as essential for piRNA-mediated gene silencing. We found the snRNA processing cellular machine termed the Integrator complex is required for both type I and type II piRNA production. Notably, we identified a role for nuclear pore and nucleolar components in promoting the perinuclear localization of anti-silencing CSR-1 Argonaute, as well as a role for Importin factor IMA-3 in nuclear localization of silencing Argonaute HRDE-1. Together, we have shown that piRNA silencing is dependent on evolutionarily ancient RNA processing machinery that has been co-opted to function in the piRNA mediated genome surveillance pathway.Metazoans guard their germlines against transposons and other foreign transcripts with PIWI-interacting RNAs (piRNAs). Due to the robust heritability of the silencing initiated by piRNAs in  C.elegans, piRNAs are able to initiate a robust silencing signal against mRNAs deemed as non-self  which can be inherited for as many as 20 generations after the loss of the initiating PIWI protein complex  that perfectly complements a 20nt site within the gfp coding sequence  after injection .piRNA targeting triggers a silencing signal that relies on siRNAs to function both with secondary Argonaute proteins called WAGOs (Worm-specific ArGOnautes) in the cytoplasm and in the nucleus. The RNA-dependent RNA polymerases that manufacture these siRNAs, termed 22G RNAs due to their possession of a 5’ guanosine and 22nt length, all exist and function in the cytoplasm ,20. Somes NRDE-3 . To idennockdown . Since inpp-7, prp-17, ints-1, or dic-1 all led to a reduction in 22G accumulation  caused over 40% GFP expression in our reporter when depleted ::eGFP::fib-1 3’ UTR + unc-119(+)] II.) and SS747 (bnIs1[pie-1::GFP::pgl-1 + unc-119(+)]) and YY538 (hrde-1 (tm1200) III.) were obtained from the Caenorhabditis Genetics Center (CGC). YY584 (ggSi1[hrde-1p::3xflag::gfp::hrde-1] II.) was a gift from Scott Kennedy. HCL199 (hrde-1 (tm1200) III; wago-10 (tm1186) V.) was outcrossed from WM191. HCL105 (gfp::csr-1 IV.) and HCL125 (gfp::prg-1 flag::mCherry::glh-1 I.) are described in (mCherry::npp-7(uoc21) I.) and HCL135 (prg-1(uoc8) oma-1::gfp I; cdk-1::gfp II; 21ur-anti-gfp(type I) IV.) are described below.Animals were grown on standard nematode growth media (NGM) plates seeded with the ribed in . CreatioE. coli HT115 (DE3) strains expressing the appropriate double-stranded RNA (dsRNA). RNAi bacterial strains were obtained from the Ahringer C. elegans RNAi Collection (Source BioScience). Bacterial cultures were grown in Luria broth supplemented with 100 μg/ml ampicillin and 50 μg/ml tetracycline overnight at 37°C. Cultures were seeded on NGM plates containing 100 μg/ml ampicillin, 50 μg/ml tetracycline, and 1 mM IPTG and incubated at room temperature until dry. L4 hermaphrodites were picked onto the plates for feeding at 20°C and removed after 24 hours. Adult progeny were screened. In instances where L4 treatment resulted in early next generation arrest, L1s were plated and screened as adults. Experiments where same generation screening was necessary are indicated in the relevant figure legend or data table. HT115 (DE3) expressing empty RNAi vector L4440 was used as the control. To assign significance to treatments in the piRNA reporter activation screen, a threshold of 20% activation was used as control treatment sometime resulted in spurious activation, but never resulted in over 20% naïve activation at 20°C for type I or type II piRNA reporters.RNA interference (RNAi) was performed by feeding animals with https://crispr.med.harvard.edu/) to design sgRNAs. The sgRNAs were cloned into pDD162 .To generate the flag::mCherry::NPP-7 donor construct, 500 bp upstream and 500 bp downstream of the GFP- and RFP/mCherry-tagged fluorescent proteins were visualized in living nematodes by mounting young adult animals on 2% agarose pads with M9 buffer  with 10 mM levamisole. Fluorescent images used for localization studies were captured using a Zeiss LSM800 confocal microscope with a Plan-Apochromat 40X/1.4 Oil objective. Fluorescent images used for screening for GFP expression were captured using a Zeiss Axio Imager M2 compound microscope with a Plan-Apochromat 40X/1.4 Oil objective.1 μg of total RNA was reverse transcribed with SuperScript IV Reverse Transcriptase (Invitrogen) in 1x reaction buffer, 2U SUPERase-In RNase Inhibitor (Invitrogen), 0.5 mM dNTPs, and 2.5 μM random hexamers. Each real-time PCR reaction consisted of 3 μL of cDNA, 1 μM forward gene-specific primer and 1 μM reverse gene-specific primer. The amplification was performed using iTaq Universal SYBR Green Supermix (Bio-Rad) on the Bio-Rad CFX96 Touch Real-Time PCR Detection System. The experiments were repeated for a total of three technical replicates.Analysis. Fastq reads were trimmed using custom perl scripts. Trimmed reads were aligned to the C.elegans genome build WS230 using bowtie ver 1.2.1.1  RNAs were enriched with mirVana miRNA Isolation Kit (Ambion). In brief, 80 μL (200–300 μg) of total RNA, 400 μl of mirVana lysis/binding buffer and 48 μL of mirVana homogenate buffer were mixed well and incubated at room temperature for 5 minutes. Then 176 μL of 100% ethanol was added and samples were spun at 2500 × g for 4 minutes at room temperature to pellet large (>200nt) RNAs. The supernatant was transferred to a new tube and small (<200nt) RNAs were precipitated with pre-cooled isopropanol at −70°C. Small RNAs were pelleted at 20,000 × g at 4°C for 30 minutes, washed once with 70% pre-cooled ethanol, and dissolved with nuclease-free water. 10 μg of small RNAs were fractionated on a 15% PAGE/7M urea gel, and RNA from 17 nt to 40 nt was excised from the gel. RNA was extracted by soaking the gel in 2 gel volumes of NaCl TE buffer  overnight. The supernatant was collected through a gel filtration column. RNA was precipitated with isopropanol, washed once with 70% ethanol, and resuspended with 15 μL nuclease-free water. RNA samples were treated with RppH to convert 22G-RNA 5’ triphosphates to monophosphates in 1x reaction buffer, 10U RppH (New England Biolabs), and 20U SUPERase-In RNase Inhibitor (Invitrogen) for 3 hours at 37°C, followed by 5 minutes at 65°C to inactivate RppH. RNA was then concentrated with the RNA Clean and Concentrator-5 Kit (Zymo Research). Small RNA libraries were prepared according to the manufacturer’s protocol of the NEBNext Multiplex Small RNA Sample Prep Set for Illumina-Library Preparation (New England Biolabs). NEBNext Multiplex Oligos for Illumina Index Primers were used for library preparation (New England Biolabs). Libraries were sequenced using an Illumina HiSeq4000 to obtain single-end 50 nt sequences at the University of Chicago Genomic Facility.  1.2.1.1  with opt 1.2.1.1 . Sense a 1.2.1.1 , which r 1.2.1.1 , reshape 1.2.1.1 , ggpubr  1.2.1.1 , dplyr  from each library. Success of cap-dependent isolation was determined by comparing the enrichment for piRNA precursors versus mature piRNAs in the cap-dependent libraries compared to the previously constructed traditional small RNA libraries.Supplement 1Supplement 2