The human Ubiquitously transcribed tetratricopeptide repeat gene, Y-linked (UTY) gene encodes histone demethylase involved in protein-protein interactions. UTY protein evidence at protein level predicted intracellular and secreted protein. UTY is also involved in spermatogenesis process.The high-risk non-synonymous single nucleotide polymorphism in the coding region of the UTY gene was screened by SNP database and identified missense variants were subjected to computational analysis to understand the effect on protein function, stability and structure by SIFT, PolyPhen 2, PANTHER, PROVEAN, I-Mutant 2, iPTREE-STAB, ConSurf, ModPred, SPARKS-X, QMEAN, PROCHECK, project HOPE and STRING.A total of 151 nsSNPs variants were retrieved in UTY gene out of which one missense variant (E18D) was predicted to be damaging or deleterious using SIFT, PolyPhen 2, PANTHER and PROVEAN. Additionally, E18D variant showed less stability, high conservation and having role in post translation modification using i-Mutant 2 and iPTREE-STAB, ConSurf and ModPred, respectively. The predicted 3D model of UTY using SPARKS-X with z-score of 15.16 was generated and validated via QMEAN (Z-score of 0.472) and PROCHECK which plots Ramachandran plot  and it indicates a good quality model. STRING showed that UTY interacts with ten different proteins.i.e. rs3212293 (E18D). Therefore, it provides useful information about functional SNPs for future prospects concerning infertility in men.This study revealed that SNP data available on database was deduced to find out the most damaging nsSNPs  Kb (i.e. Ubiquitin-specific protease 9 (USP9Y), Dead box on the Y (DBY) and ubiquitously transcribed tetratricopeptide repeat gene, Y-linked (UTY).Infertility is a major public health problem in which approximately 15% of couples are not able to conceive an offspring . GeneticKb . AZFa suIt has been reported that human UTY gene is located to band 5C corresponding to AZFa sub-region and this band contains one or more genes that have function in spermatogenesis process and Y-specific growth gene . The corSingle nucleotide polymorphism (SNP) is a variation of single nucleotide which can be found anywhere throughout the genome . Mostly It is believed that autosomal and sex chromosomal abnormality accounts for 10–15% of cases in men with infertility . FurtherGenomic analysis of UTY was conducted using sorting intolerant from tolerant (SIFT), PolyPhen 2, protein variation effect analyzer (PROVEAN) and protein analysis through evolutionary relationships (PANTHER) to retrieve deleterious mutation followed by stability analysis using I-Mutant 2 and iPTREE-STAB. Additionally, conservation analysis prediction (ConSurf), 3D structure prediction, validation of model, structural effect and protein-protein interaction were done for UTY. Details of each tool are described below and highlighted in http://www.ncbi.nlm.nih.gov) and related information regarding UTY gene and protein was collected from Online Mendelian Inheritance in Man (OMIM- https://www.omim.org/entry/400009).UTY SNPs (rsIDs) related data and protein sequence were extracted from the National Center for Biotechnology Information  tool predicts the single amino acid change impact on protein function that would be either tolerated or damaging. The SIFT score will be below or equal to 0.05 when amino acid substitution is predicted damaging and if the score is above 0.05, amino acid substitution is predicted to be tolerating . PolyPhePANTHER cSNP  predicts functional effect of amino acid substitution on protein by calculating subPSEC (Substitution position-specific evolutionary conservation) score on the basis of alignment of evolutionary related proteins .PROVEAN  predicts the effect of any type of protein sequence variation consisting of amino acid substitution and in-frame insertion and deletion change on proteins biological function. It analyzes the nsSNPs as deleterious or neutral when the score is below or above the threshold, respectively .Generally, mutation leads to structural stability changes which affect the function of protein. Therefore, the stability check was carried out using I-Mutant 2 and iPTREE-STAB. I-Mutant 2 server prediction is based on support vector machine (SVM) for protein stability change of the folded protein in relation with single point mutation. It predicts direction (Δ ΔG sign) as well as Δ ΔG associated values of the protein stability changes upon single site mutations. iPTREE-STAB server is based on decision tree where it predicts the stability change  due to single site mutation. Thus, it predicts whether the outcome of the amino acid substitution is stabilizing or destabilizing .Conservation prediction of UTY amino acids was analyzed using ConSurf tool. It uses high-throughput characterization of functional regions of the protein. The degree of conservation of amino acid is calculated based on conservational score in the scale of 1–9 where 1–3 scores are variable, 4–6 scores indicate average conservation and 7–9 scores indicate high conservation .In UTY protein, post translational modification (PTM) sites were sequence based, predicted using ModPred. It accommodated 34 ensembles of logistic regression models that were trained independently on a united set of 126,036 non-redundant experimentally verified sites for 23 distinct polymorphism, retrieved from pubic databases and an ad-hoc literature investigation .SPARKS-X is a single method fold recognition technique which gives 3D structure. This tool was improved by changing the alignment scoring function as well as adding the SPINE-X techniques which upgraded prediction of secondary structure, backbone torsion angle and solvent accessible surface area .The predicted model of UTY from SPARKS-X was validated using qualitative model energy analysis (QMEAN) and PROCHECK. QMEAN z-score was another tool used for quality assessment where it analyzed the degree of nativeness of the predicted 3D structure of protein. The QMEAN score imitated the predicted global model reliability ranging between 0–1 . PROCHECProject HOPE was used to know the structural effect where it was used for molecular dynamics simulation to analyze the single point mutation. First BLAST was performed against PDB and built a homology model and retrieved tertiary structure information through WHATIF followed by the access to UniProt database features. Furthermore, the protein features were predicted using Distributed Annotation System (DAS) server , 36.Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database provides a critical assessment and integration of protein-protein interactions whether it is direct  or indirect  association .1 database and contained 151 missense, 4 non-sense, 2 splice site at 3′ end, 73 UTR at 3′ end, 1 splice site at 5′ end, 5 UTR at 5′ end, 84 coding synonymous, 6 frame shift, 2302 introns and 4 stop gained SNPs. Only non-synonymous coding SNPs of UTY were selected for this investigation.The polymorphism data for UTY gene investigated in present work was retrieved from NCBI dbSNPThe UTY single nucleotide variants were administrated to computational analysis through variety of tools.According to SIFT, a total of 5 out of 151 nsSNPs of UTY gene from dbSNP analysis were predicted to be tolerated or deleterious . SIFT clTo increase the accuracy of computational techniques to compute utmost deleterious SNPs, different computational methods were used. Therefore, PANTHER and PROVEAN were used for further analysis. Out of 5 variants, one (E18D) was predicted to be damaging with SIFT and PolyPhen 2. PANTHER was performed to verify validation of results obtained from two tools and E18D was portrayed damaging with SIFT, PolyPhen 2 and now with PANTHER too . E18D waProtein stability of UTY variant upon point mutation was found using I-Mutant 2.0 and iPTREE-STAB. E18D was subjected for stability prediction and showed decreased stability by both the tools .Amino acids are highly conserved when located in biologically active sites. If there is substitution of these amino acids, it leads to complete loss of biological activities . For thiTo analyze the effect of nsSNPs on post translational modification process of human UTY protein, Mod Pred tool was used. ModPred predicted sites for proteolytic cleavage, carboxylation and ADP-ribosylation at E18 .SPARKS-X modeled the 3D structure of UTY where 10 full length models were generated. The quality of full length models were predicted by z-score (Z-score >6). Using SPARKS-X, native model as well as mutated model with the most deleterious variant (E18D) was generated .QMEAN tool was used where z-score gives prediction about the absolute quality of the model . The quaSPARKS-X predicted model was validated using another tool known as PROCHECK where Ramachandran plot was used. Out of all the residues of UTY protein, 1024 (85.3%) were in most favorable regions, 148 (12.3%) were in additionally allowed regions, 24 (2.0%) were in generously allowed regions and 5 (0.4%) were in disallowed regions . TherefoProject HOPE was used to know the structural changes but no such information was retrieved. Therefore, HOPE used UniProt database and Reprot software was used to predict the mutational analysis. E18D substitution results into a change in glutamic acid residue to an aspartic acid residue at position 18 . The mutkDa alpha, class A member 1 (HSP90AA1); heat shock protein 90 kDa alpha, class B member 1 (HSP90AB1); ubiquitin specific peptidase 9, Y-linked (USP9Y); lysine (K)-specific demethylase 5D (KDM5D); histone deacetylase 2 (HDAC2); histone deacetylase 1 (HDAC1); histone deacetylase 3 (HDAC3); SET domain containing 2 (SETD2); DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked (DDX3Y) and sex determining region Y (SRY) (STRING prediction indicated that UTY interacts with heat shock protein 90  Y (SRY) .Single nucleotide polymorphism is the important variant as it accounts for large number of inherited diseases. To functionally distinguish neutral and disease associated SNPs is of great importance. Screening and identification of variants which in turn is responsible for particular phenotypes through molecular approaches is likely to be time consuming, tedious and expensive , 40. As The AZFa sub-region comprises three candidate genes- DDX3Y (former name DBY), USP9Y and UTY  and mutaThe results obtained in the present study revealed that utilization of various algorithms serves as a dynamic tool to estimate or identify candidate functional nsSNPs. According to Thusberg and Vihinen (2009) and Hicks et al. (2011), SIFT and PolyPhen 2 have been reported as better performing tools to identify most deleterious nsSNPs , 46. I-MThe ConSurf tool output indicated that the nsSNP at position E18 was predicted in highly conserved region and may have promising role in UTY protein function. In addition, the E18 residue showed PTM sites for proteolytic cleavage, carboxylation and ADP-ribosylation. Therefore, amino acid polymorphism at E18 position is likely to have impact on post translational modification process of human UTY protein.For UTY protein, 3 dimensional models were generated using SPARKS-X to visualize deviation between the wild type and mutant type protein models. According to HOPE in E18D, the wild type protein framework was disturbed due to shift of glutamic acid to aspartic acid upon missense mutation which may cause loss of interaction.Validation of SPARKS-X generated model using QMEAN and PROCHECK servers indicated that model of UTY is good for further experiment as well as for better understanding of biological activity of UTY protein. Project HOPE was used to understand structural changes upon amino acid polymorphism. The wild and mutant amino acids differ sometimes due to specific properties which can disturb the structural and/or functional features of the native protein . SimilarThe present study symbolized the first detailed analysis where sequence and structure based algorithms are used to identify functional nsSNPs in UTY gene. Out of 151 nsSNPs, 1 (0.66%) was predicted to be deleterious by SIFT and PolyPhen 2. Further analyses support that E18D variant is predicted to cause change in stability and functional interaction performance of UTY protein. This computational analysis of UTY will assist to understand and design future experimental research.


The Gene Ontology (GO) :25–29, 2000) is a powerful tool in the informatics arsenal of methods for evaluating annotations in a protein dataset. From identifying the nearest well annotated homologue of a protein of interest to predicting where misannotation has occurred to knowing how confident you can be in the annotations assigned to those proteins is critical. In this chapter we explore what makes an enzyme unique and how we can use GO to infer aspects of protein function based on sequence similarity. These can range from identification of misannotation or other errors in a predicted function to accurate function prediction for an enzyme of entirely unknown function. Although GO annotation applies to any gene products, we focus here a describing our approach for hierarchical classification of enzymes in the Structure-Function Linkage Database (SFLD) :D521–530, 2014) as a guide for informed utilisation of annotation transfer based on GO terms. Enzymes are the biological toolkit that organisms use to perform the chemistry of life, and the Gene Ontology (GO)  represensee Chap. 19 -arginine N-methyltransferase (EC 2.1.1.124) is much more complex see.see Chap. 2 [Formally, MFO terms describe the activities that occur at the molecular level; this includes the “catalytic activity” of enzymes or “binding activity”. It is important to remember that EC numbers and MFO terms represent activities and not the entities  that perform them. Further, they do not specify where, when or in what context the action takes place. This is usually handled by the Cellular Component Ontology. The final ontology in GO, the Biological Process Ontology (BPO), provides terms to describe a series of events that are accomplished by one or more organised assemblies of molecular functions. Each MFO term describes a unique single function that means the same thing regardless of the evolutionary origin of the entity annotated with that term. Although the BPO describes a collection of activities, some BPO terms can be related to their counterparts in the MFO, e.g. GO:0009102 (biotin bio-synthetic process) could be considered to be subsumed with the MFOtermGO:0004076(biotinsynthaseactivity)asGO:0009102 includes the activity GO:0004076, i.e. in such cases, the terms are interchangeable for the purpose of evaluation of a protein’s annotation. Please see Chap. 10 [As a protein, an enzyme has many features that can be described and used to define the enzyme’s function, from the primary amino acid sequence to the enzyme’s quaternary structure , the chemistry that is catalysed, to the localisation of the enzyme. Features can also denote the presence (or absence) of active site residues to confirm (or deny) a predicted function, such as EC class, using the compositional makeup of a protein amino acid sequences , 9. NeveChap. 10  for a moAlthough there are many different features and methods that can (and are) used to predict the function of a protein, there are several advantages to using GO as a broadly applied standard. Firstly, GO has good coverage of known and predicted functions so that nearly all proteins in GO will have at least one associated annotation. Secondly, annotations associated with a protein are accompanied by an evidence code, along with the information describing that evidence source. Within the SFLD  each annFully manually curated: These proteins will usually have an associated experimental evidence that has been identified by human curators and who have added relevant evidence codes. For the purposes of the SFLD and this chapter, these are considered high confidence and will have a greater weight than any other annotation confidence level.Computational with some curator input: These are computationally based annotations that have been propagated through curator derived rules, and are generally considered to be of medium confidence by the SFLD. Due to the huge proportion of sequences in large public databases now available, over 98 % of GO annotations are inferred computationally .Computational with no curator input: These annotations that have been computationally inferred from information without any curator input into the inference rules and are considered to be of the lowest confidence by the SFLD.see Misannotation Section 1.4).All computationally derived annotations rely upon prior knowledge, and so if the rule is not sufficiently detailed, it can still lead to the propagation of annotation errors  superfamily as the largest grouping of enzymes for which a common ancestry can be identified. Superfamilies can be defined in many different ways, and every resource that utilises them in the bioinformatics community has probably used a slightly different interpretation and method to collate their data. However, they can be broadly classified as structure- based, in which the three-dimensional structures of all available proteins in a superfamily have been aligned and confirmed as homologous, or sequence based, where the sequences have been used rather than structures. Many resources use a combination of approaches. Examples of superfamily based resources include CATH , Gene3D http://sfld.rbvi.ucsf.edu/) is a manually curated classification resource describing structure-function relationships for functionally diverse enzyme superfamilies [a similarity threshold at which all members of the subgroup have more in common with one another than they do with members of another subgroup.  superfamily, the cognate reaction is often not known as the assays performed use a relatively standard set on non-physiological substrates to infer the type of reaction catalysed by each enzyme that is studied. Moreover, GSTs are often highly promiscuous for two or more different reactions again complicating function assignment . That beSequence similarity networks (SSNs) are a key tool that we use in the Structure-Function Linkage Database (SFLD) as they give an immediately accessible view of the superfamily and the relationships between proteins in this set. This in turn allows a user to identify boundaries at which they might reasonably expect to see proteins performing a similar function in a similar manner. As was shown in From the networks shown previously, a user may intuitively see that there are three basic groups of proteins. Further, it could be hypothesised that these groups could have different functions (which is indeed the case in this particular example). Thus, the user may be left with the question: How do I know what boundaries to use for high confidence in the annotation transfer? E-values (used as scores) between the source and target sequences. Using simple metrics such as these, relatively small networks are trivial and fast to produce from a simple all-against-all BLAST calculation. However, the number of edges produced depends on the similarity between all the nodes to each other, so that for comparisons of a large number of closely related sequences, the number of edges will vastly exceed the number of nodes, quickly outpacing computational resources for generating and viewing networks. As a result, some data reduction will eventually be necessary. The SFLD uses representative networks where each node represents a set of highly similar sequences and the edges between them represent the mean E-value similarity between all the sequences in the source node and all the sequences in the target node. As shown in SSNs provide a visually intuitive method for viewing large sets of similarities between proteins . AlthougA common use of GO enrichment analysis is to evaluate sets of differentially expressed genes that are up- or down-regulated under certain conditions . The resThe simplest method and least rigorous, is to take the set of proteins being evaluated, count up the number of times a single annotation occurs  and up-weight for experimental (or high confidence) annotations. Then, by dividing by the number of proteins in the set, any annotation with a ratio greater than one can be considered “significant”.see Chap. 13 [A more rigorous treatment assumes that for a set of closely related proteins (i.e. belonging to a family) a specific GO term is said to be over-represented when the number of proteins assigned to that term within the family of interest is enriched versus the background model as determined by a probability distribution. Thus, there are two decisions that need to be made, firstly, identifying the background model and then which probability function to use. The background model is dependent on the dataset and the question that is being asked. For example in the SFLD model, we might use the subgroup or superfamily and a random background model that gives us an idea of what annotations could occur purely by chance. The lack of high  confidence annotations is another complication in examining enrichment of terms. If one is using IEA annotations to infer function, the assertions can quickly become circular (with inferred annotations being transferred to other proteins which in turn are used to annotate yet more proteins), leading to results which themselves are of low confidence. Similarly, if very few proteins are explicitly annotated with a high/medium confidence annotation, the measure of significance can be skewed due to low counts in the dataset. The choice of the probability function is also going to depend somewhat on what question is being asked, but the hypergeometric test (used for a finite universe) is common in GO analyses , 40. ForChap. 13 .Instead of simply transferring annotations utilising sequence homology and BLAST scores, many tools are now available  have a similar vocabulary to their parents. The nature of the GO DAG means that a protein with a function A will also inherit the more generic functions that appear higher up in the DAG; this can be one or more functions, depending on the DAG. For example, an ion transmembrane transporter activity (GO:0015075) is a term similar to voltage-gated ion channel activity (GO:0005244), the latter of which is a descendent of the former, albeit separated by the ion channel activity (GO:0005216) term. Thus, the ancestry and semantic similarity lends greater weight to the confidence in the annotation.see Pesquita et al. [Such similarity measures can be used instead of (or in conjunction with) sequence similarity measures. Indeed, it has been shown  that thea et al.  and Chapa et al. .E-value (or Bit Score) to transfer annotation, but the presence of the conserved key residues. As mentioned earlier, one key advantage to using GO annotations over those of some other resources is the evidence code (and associated source of that evidence) as shown in seeIn the example shown in Another way of assessing the veracity of the annotation transferred to a query protein is to examine both the annotations of the proteins that are closest to it in similarity as well as other entirely different types of information.One example of such orthogonal information is the genomic context of the protein. It can be hypothesised that if a protein occurs in a pathway, then the other proteins involved in that pathway may be co-located within the genome . This asseeAnother type of orthogonal information that can be used can be deduced from protein domains present in a query protein and their associated annotations—what are the predicted domains present in the protein, do they all match the assigned function or are there anomalies. A good service for identifying such domains is InterProScan . FurtherA significant challenge with using SSNs to help evaluate GO annotations is that SSNs are not always trivial to use without a detailed knowledge of the superfamilies that they describe. For example, choosing an appropriate threshold for drawing edges is critical to obtaining network clustering patterns useful for deeper evaluation. In Even using the powerful tools and classifications provided by GO, interpreting protein function in many cases requires more in-depth analysis. For several reasons, it is not always easy to confidently determine that a protein is not correctly annotated. Firstly, how closely related is the enzyme to the group of interest? Perhaps we can only be relatively certain of its superfamily membership, or maybe we can assign it to a more detailed level of the functional hierarchy. If it fits into a more detailed classification level, how well does it fit? At what threshold do we begin to see false positives creeping into the results list? Using networks, we can also examine the closest neighbours that have differing function and ask whether there are similarities in the function , it is often not included in the electronic inference of annotations. It is important to note, however, that there are evolutionary events that may “scramble” the sequence, leaving it unclear to an initial examination whether the residues are conserved or not. A prime example is the case in which a circular permutation has occurred. Thus, it is important to look at whether there are other residues (or patterns of residues) that could perform the function of the “missing” residues. It is also possible that conservative mutations have occurred, and these may also have the ability to perform the function of the “missing” residues .http://www.moonlightingproteins.org/) [Another consideration with function evaluation is the occurrence of moonlighting proteins. These are proteins that are identical in terms of sequence but perform different functions in different cellular locations or species; for example argininosuccinate lyase (UniProtKB id P24058) is also a delta crystalline which serves as an eye lens protein when it is found in birds and reptiles . A good ns.org/) . Such cans.org/) , 57.Finally, does the protein have a multi-domain architecture and/ or is it part of a non-covalent protein-protein interaction in the cell? An example of a functional protein requiring multiple chains that are transiently coordinated in the cell is pyruvate dehydrogenase (acetyl-transferring) (EC 1.2.4.1). This protein has an active site at the interface between pyruvate dehydrogenase E1 component subunit alpha (UniProtKB identifier P21873) and beta (UniProtKB identifier P21874), both of which are required for activity. Thus, transfer of annotation relating to this function to an unknown  needs to include both proteins. Similarly, a single chain with multiple domains, e.g. biotin biosynthesis bifunctional protein BioAB (UniProtKB identifier P53656), which contains a BioA and BioB domain, has two different functions associated with it. In this example, these two functions are distinct from one another so that annotation of this protein only with one function or the other could represent a type of misannotation .In some cases, proteins are annotated by some type of “plurality voting”. Plurality voting is simply assuming that the more annotations that come from different predictors, the more likely these are to be correct. As we have shown in this chapter (and others before us ), this iExperimentalists simply can’t keep up with the huge volume of data that is being produced in today’s high-throughput labs, from whole genome and population sequencing efforts to large-scale assays and structure generation. Almost all proteins will have at least one associated GO annotation, and such coverage makes GO an incredibly powerful tool, especially as it has the ability to handle all the known function information at different levels of biological granularity, has explicit tools to capture high-throughput experimental data and utilises an ontology to store the annotation and associated relationships. Although over 98 % of all GO annotations are computationally inferred, with the ever-increasing state of knowledge, these annotation transfers are becoming more confident  as rule-


It has a user-friendly web interface that can be easily queried with external protein identifiers. With unique integration of prominent databases including NCBI, UniProtKB, Swiss-Prot, Pubmed, PDB, STRING, IntAct, KEGG, Pfam and Protein Mutant Database, it provides detailed information on PDZ interactome apart from the customized BLAST option. Most importantly, this database encompasses the mutations and diseases associated with PDZ containing proteins manually curated by our group, thus making it a comprehensive compilation. It also features tools to query the database using sequence (PDZ-Blast) and to find if protein of interest is a PDZ-binding protein. PDZscape is freely available at http://www.actrec.gov.in:8080/pdzscape.PDZ-containing proteins comprise one of the most widely distributed protein families playing major role in localization and membrane receptor clustering. They are hence important regulators of signal transduction in cellular pathways. Although knowledge on these proteins has increased exponentially, the existing database ‘PDZBase’ is limited by presence of only 339 proteins as it dates back to 2004 when very little data was available. Thus, lack of exclusive information on this protein family led us to develop PDZ Post synaptic density protein (PSD95), Drosophila disc large tumor suppressor (Dlg1), and Zonula occludens-1 protein (zo-1)) - a ubiquitous 70–90 residue protein–protein interaction module. PDZ helps localization and clustering of membrane receptors by binding primarily to their C-terminus in different pathways that drive life [The concept of domains has been one of the core themes in protein studies; one such being PDZ , Protein Mutant Database (PMDB), DisGENET, STRING, IntAct, Pfam and KEGG lead to detailed information about PDZ proteins and pathways they are involved in, along with the specific nature of the interaction and the methodology involved. BLAST has been customized for PDZ protein homology search with an additional option of batch downloading the PDZscape. The organization of the database along with its different features is compiled in a self-explanatory flowchart as shown in Fig. The database MySQL Administrator Tool (Workbench 6.0 CE), Eclipse, Apache Tomcat 9 server, Java 1.8.scape. The entire PDZ domain containing data was manually downloaded from Uniprot-KB as flat files and the other related information was extracted out in a CSV  file using Perl regular expressions. This CSV file was further used for the creation of tables in a database using MySQL server. Various information on PDZscape proteins such as their Gene information-Gene IDs, structural information-PDB IDs [scape output. Extensive manual curation was also performed for obtaining information on the association of PDZ containing proteins with different pathological conditions. Currently, more than 300 entries have been curated with the known mutations and their association with various diseases. This information is available for Human PDZ proteins and will be periodically updated. Manual curation was performed using literature search on PubMed and filtering the papers based on proven disease associations from experimental studies. Literature reports for proteins, where only mutations have been reported without any information on associated diseases, have also been included in the database wherever relevant. It has been observed that disease-association in PDZ-containing proteins is not limited to mutations but depends on other factors as well that include over-expression, chromosomal deletion, inhibition etc. These diseases have also been reported in this database. The entire data takes into consideration all PDZ proteins and can be retrieved in ‘.csv’ format from download page.The PDZ protein data and related information such as sequence, organism information, available structures, localization and related literature were obtained from publicly available Uniprot-KB/Swissprot (release 2017_20) database. Uniprot-KB/Swisprot being the largest and up-to-date manually curated database of proteins was the most important data source for PDZ-PDB IDs , protein-PDB IDs  and Path-PDB IDs  that is -PDB IDs , 13 feat-PDB IDs , 13 wereTo facilitate reverse search and find whether the protein of interest is a PDZ-interacting partner, a simple tool, based on sequence similarity and ID mapping has been developed in Java, which takes UniProt ID of a protein as an input and finds whether it is a PDZ-binding protein. In both protein and peptide mode, this tool scans the given sequence for presence of known PDZ-binding motif, which are stored as regular expressions, based on reports from the literature . PBPFindscape database was developed with JavaScript using Eclipse Juno software development environment. For data integration and parsing, programs were written in JavaScript and Perl. These programs were used to search and parse the data on PDZ proteins and their interacting partners from flat files to create output files in MySQL Tables. All MySQL queries to the databases were implemented in a Javascript page using Java-based data access technology (JDBC) connection and have been uploaded on Linux-based TomCat 9.0 Apache server. The linking of entire data integration server was done using Eclipse Software Development Environment (SDE). Eclipse is a multi-language Integrated Development Environment (IDE) comprising a base workspace and an extensible plug-in system for customizing the environment, which is widely used for database and software development. This database has been constructed using various sources of PDZ-containing protein sequences, and information on their structure and interacting partners. The information so compiled not only includes well established interacting partners but also putative ones that can prove to be useful leads for further investigation by researchers. This wide range of data was amalgamated together to form a new and comprehensive knowledge-base for PDZ proteins.PDZscape Database is available online through ACTREC Home Page (http://www.actrec.gov.in/) under ‘Database & Tools’ (http://www.actrec.gov.in:8080/pdzscape) and has an easy-to-browse web interface. End user can retrieve complete information of a particular protein entry using any of the following: Keywords, Uniprot IDs, String IDs, PDB IDs, organism names or protein sequence. Search tool provides easy access to the database using various criteria as shown in Fig. scape using URL: UniProtIDhttp://www.actrec.gov.in:8080/pdzscape/protein.jsp?accID=, where term UniProtID should be replaced by protein identifier of required PDZ-protein. Figure scape using sequence of the protein of interest, which is accessible to the end-user through the hyperlink provided in the main menu. The query sequence can thereby be entered either manually by pasting FASTA sequence in the respective search box or by uploading a FASTA sequence file. Results can be downloaded in the pdf format from the BLAST result page. ‘Download’ option can be used to retrieve the complete database in CSV format; customized subsets can also be downloaded from this link. The links provided in the database highly expand the annotations of the PDZscape, and will enable end-user to have access to every bit of required information on a single arena. PDZscape is designed to include periodic updates and inclusion of new features to make it a useful portal for biologists. PBPFinder currently works on regular expressions for motifs described in previous literature. Development of robust machine learning model for prediction of PDZ-binding proteins is in process and will be a valuable addition to this database. While PDZbase is a well-known portal for PDZ information, it contains a limited number of PDZ-proteins with focus on the details of protein-protein interactions that perhaps needs updating. On the other hand, PDZscape encompasses all PDZ-proteins with known and putative binding partners.PDZA large number of genes have been annotated over the last decade with the exponential increase in the number of depositions on PDZ proteins in NCBI and other sources. This provides an exciting as well as challenging prospect of studying these newly identified and annotated proteins under a single platform. The plethora of research endeavors that have undertaken in the last decade on this protein domain underscores its importance in critical cellular processes and association with several disease conditions. Categorizing proteins, though important is a relatively simple job compared to their structural and functional characterization. With large number of PDZ-proteins being identified and structurally characterized Fig. , there wThe existing PDZ domain database, PDZBase currently contains approximately 339 proteins and their interactions . The infhttp://www.actrec.gov.in:8080/pdzscape/.Database is freely available for public use at


Different types of PPR proteins, with different tasks in RNA editing, have been suggested to interact in an editosome to complete RNA editing. Other non-PPR editing factors, such as the multiple organellar RNA editing factors and the organelle RNA recognition motif-containing protein family, for example, have also been described in plants. However, while evidence on protein interactions between non-PPR RNA editing proteins is accumulating, very few PPR protein interactions have been reported; possibly due to their high instability. In this manuscript, we aimed to optimize the conditions for non-denaturing protein extraction of PPR proteins allowing in vivo protein analyses, such as interaction assays by co-immunoprecipitation. The unusually high protein degradation rate, the aggregation properties and the high pI, as well as the ATP-dependence of some PPR proteins, are key aspects to be considered when extracting PPR proteins in a non-denatured state. During extraction of PPR proteins, the use of proteasome and phosphatase inhibitors is critical. The use of the ATP-cofactor reduces considerably the degradation of PPR proteins. A short centrifugation step to discard cell debris is essential to avoid PPR precipitation; while in some cases, addition of a reductant is needed, probably caused by the pI/pH context. This work provides an easy and rapid optimized non-denaturing total protein extraction protocol from plant tissue, suitable for polypeptides of the PPR family.Pentatricopeptide repeat proteins are one of the major protein families in flowering plants, containing around 450 members. They participate in RNA editing and are related to plant growth, development and reproduction, as well as to responses to ABA and abiotic stresses. Their characteristics have been described  Samples were taken 3–6 days after infiltration.Proteins were expressed as previously described . 2-day c plasmid  were pelN. benthamiana leaves). The initial extraction buffer used in this manuscript was slightly modified from the one used previously for chloroplastic PPR immunoprecipitation from stable transformed A. thaliana plants  for 5 min at 65°C, immediately after extraction. 30 μl of the protein samples were run in an 8% SDS-PAGE gel and blotted on a 0.45 μm nitrocellulose membrane (Amersham Protran) at 4°C. The membrane was stained with 0.1% Ponceau S in 1% acetic acid for 5 min. The background stain was removed by treatment with 1% acetic acid during 15 min before imaging. The Ponceau S was completely removed with T-TBS buffer . The membrane was probed as previously indicated , with 1:% CoCl2) . Both anS1 FigProtein ladder shown in the first run , 55, 40, 35, 25 and 15 kDa). The respective molecular weights were: SLO2-HA, 66.41 kDa; DYW2-GFP, 92.56 kDa; MEF57-GFP, 100.62 kDa; and HSP60.3B-GFP, 87.42 kDa.(PDF)Click here for additional data file.S2 FigHistograms showing the GFP/HA-tagged protein amount relative to Ponceau, according to band intensity in the blots.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.Nerve tissue contains a high density of chemical synapses, about 1 per µmDOI:http://dx.doi.org/10.7554/eLife.26414.001 Each nerve cell in the brain of a mammal communicates with about 1,000 other nerve cells in a complex network. Nerve cells ‘talk’ to each other via structures called synapses that connect the nerve cells together. The number of synapses in the brain is enormous – for example, a human brain contains about one quadrillion synapses.One technique that can be used to look at the synapses in the brain is called 3D electron microscopy. The huge number of synapses in an image makes it impractical for researchers to manually label them. However, current methods that use computers to automatically label synapses work most accurately only on images that are so detailed that they cover only very small volumes of the brain (much less than 1 cubic millimeter).Staffler et al. have now developed a new method, called SynEM, that makes it possible for computers to do all the work of finding the synapses in larger volumes of the brain. Without any input from researchers, SynEM can correctly identify connections between nerve cells 97% of the time, which is far more successful than any other current computer-based approach. Importantly, SynEM also automatically indicates which nerve cells are connected by a given synapse, providing a map of “who talks to whom” across the brain.Together with SynEM, methods that track the cable-like structures  that nerve cells grow to find other nerve cells are already allowing us to map the communication networks in the brain. In the far future Staffler et al. hope that such mappings will become so routine that entire human brains could be studied, perhaps to investigate how diseases affect them.DOI:http://dx.doi.org/10.7554/eLife.26414.002 Typical sources of errors are vesicle clouds close to membranes that target nearby neurites . This would imply a runtime of 279.9 days for a large 1 mm3 dataset, which is comparable to the time required for current segmentation methods, but much faster than the currently required human annotation time .Finally, SynEM is sufficiently computationally efficient to be applied to large connectomics datasets. The total runtime on the 384592 μmnn synapses as connected , a neuron-to-neuron connectivity recall Rnn of 97% can be achieved with a synapse detection recall Rs of 65.1%  we obtained a statistical estimate of connectome error rates based on synapse detection error rates. We assume that the goal is a binary connectome containing the information whether pairs of neurons are connected or not. Automated synapse detection provides us with weighted connectomes reporting the number of synapses between neurons, from which we can obtain binary connectomes by considering all neuron pairs with at least γonnected . Synaptionnected . The effstimated  for eachs recall .10.7554/nn then follows from the total number of synapses in the connectome Nsyn = N2 × cr×<nsyn>, with cr the pairwise connectivity rate, about 20% for local excitatory connections in cortex . The number of false positive (FP) synapse detections was deduced from TP and the synapse precision Ps as FP=TP×(1-Ps)/Ps, yielding Rs×Nsyn×(1-Ps)/Ps false positive synapse detections. These we assumed to be distributed randomly on the connectome and estimated how often at least γnn synapses fell into a previously empty connectome entry. These we considered as false positive connectome entries, whose rate yields the binary connectome precision Pnn . At Rnn of 97.1%, SynEM yields a neuron-to-neuron connection precision Pnn of 98.5% s), the higher number of synapses per connected cell pair (n(i)syn is on average about 6, (i)nn; this result is stable against varying underlying inhibitory connectivity rates cri = 20%..80%, see ranges indicated in For the treatment of inhibitory connections, we followed the notion that synapse detection performance could be optimized by restricting classifications to interfaces established by inhibitory axons . SynEM detected 195644 synapses, i.e. a synapse density of 1.02 synapses per µm3, consistent with previous reports , consistent with previous reports , (EW) 0.284 ± 0.275 µm2 . This indicates that, first, synapse detection in our lower-resolution SBEM data  yields similar synapse size distributions as in the higher-resolution data in We then measured the size of the axon-spine interface of SynEM detected synapses in the test set . We findWe report SynEM, a toolset for automated synapse detection in EM-based connectomics. The particular achievement is that the synapse detection for densely mapped connectomes from the mammalian cerebral cortex is fully automated yielding below 3% residual error in the binary connectome. Importantly, SynEM directly provides the location and size of synapses, the involved neurites and the synapse direction without human interaction. With this, synapse detection is removed as a bottleneck in large-scale mammalian connectomics.Evidently, synapse detection is facilitated in high-resolution EM data, and becomes most feasible in FIB-SEM data at a resolution of about 4–8 nm isotropic . Yet, onIn addition to high image resolution, recently proposed special fixation procedures that enhance the extracellular space in 3D EM data  are repoFinally, our approach to selectively classify interfaces of inhibitory axons  requiresTogether, SynEM resolves synapse detection for high-throughput cortical connectomics of mammalian brains, removing synapse detection as a bottleneck in connectomics. With this, SynEM renders the further acceleration of neurite reconstruction again the key challenge for future connectomic analysis.3 (4 km per mm3 and dendrite path length density of 1 km per mm3 (3 (3 for synapse annotation by volume search (estimated form the test set annotation) and an effective interaction time of 60 s per identified bouton for axon-based synapse search. All annotation times refer to single-annotator work hours, redundancy may be increased to reduce error rates in neurite and synapse annotation in these estimates  and a filter size f =  and normalizing the resulting filter by the sum over all its elementsat integer coordinates  First and second order derivatives of Gaussian filters were defined asσ and evaluation of derivatives of Gaussian filters was done on U as described above. Filters were applied to the raw data I via convolution  gD of the image’s Gauss derivatives. Since S is symmetric, only the diagonal and upper diagonal entries were determined, the eigenvalues were calculated and sorted by increasing absolute value.and analogously for the other dimensions, with standard deviation σThe Hessian matrix was defined as the matrix of second order Gaussian derivatives:and analogously for the other dimensions. Eigenvalues were calculated as described for the Structure tensor.The local entropy feature was defined aswhere p(L) is the relative frequency of the voxel intensity in the range {0, …, 255} in a given neighborhood U of the voxel of interest .Local standard deviation for a voxel at location  was defined byfor the neighborhood U of location  with |U| number of elements and calculated using MATLABs stdfilt function.r with radius r around the voxel of interest, withSphere average was defined as the mean raw data intensity for a spherical neighborhood U3 is the 3 dimensional integer grid; x,y,z are voxel indices; z anisotropy was approximately corrected.where ZThe intensity/variance feature for voxel location  was defined asfor the neighborhood U of location .The set of parameters for which filters were calculated is summarized in 11 shape features were calculated for the border subvolume and the two 160 nm-restricted subvolumes, respectively. For this, the center locations (midpoints) of all voxels of a subvolume were considered. Shape features were defined as follows: The number of voxel feature was defined as the total number of voxels in the subvolumes. The voxel based diameter was defined as the diameter of a sphere with the same volume as the number of voxels of the subvolumes. Principal axes lengths were defined as the three eigenvalues of the covariance matrix of the respective voxel locations. Principal axes product was defined as the scalar product of the first principal components of the voxel locations in the two 160 nm-restricted subvolumes. Voxel based convex hull was defined as the number of voxels within the convex hull of the respective subvolume voxels .3 of the dataset were selected (39 sized 5.6 × 5.6 × 5.6 μm3 each and one of size 9.6 × 6.8 × 8.3 μm3). Then, all interfaces within these volumes were extracted as described above. Interfaces with a center of mass less than 1.124 µm from the volume border were not considered. For each interface, a raw data volume of size (1.6 × 1.6 × 0.7–1.7) μm3, centered on the center of mass of the interface voxel locations was presented to the annotator. When the center of mass was not part of the interface, the closest interface voxel was used. The raw data were rotated such that the second and third principal components of the interface voxel locations  defined the horizontal and vertical axes of the displayed images. First, the image plane located at the center of mass of the interface was shown. The two segmentation objects were transparently overlaid  from the dataset that contained no soma or dominatingly large dendrite. One volume was not used because of unusually severe local image alignment issues which are meanwhile solved for the entire dataset. The test volume had the bounding box  in the dataset. First, the volume was searched for synapses ; a synapse was considered missed if no overlapping interface of a given synapse was detected (FNs); and a detection was considered false positive (FP) if the corresponding interface did not overlap with any labeled synapse.The labels for inhibitory-focused synapse detection were generated using skeleton tracings of inhibitory axons. Two expert neuroscientists used these skeleton tracings to independently detect all synapse locations along the axons. Agreeing locations were considered synapses and disagreeing locations were resolved jointly by both annotators. The resulting test set contains 171 synapses. Afterwards, all SegEM segments of the consensus postsynaptic neurite were collected locally at the synapse location. For synapse classification all interfaces in the dataset were considered that contained one SegEM segment located in one of these inhibitory axons. Out of these interfaces all interfaces were labeled synaptic that were between the axon and a segment identified as postsynaptic. The calculation of precision and recall curves was done as for the dense test set (see above) by considering a synapse detected if at least one interface overlapping with it was detected by the classifier (TPs); a synapse was considered missed if no interface of a synapse was detected (FNs); and a detection was considered false positive (FP) if the corresponding interface did not overlap with any labeled synapse.3 that were centered in five of the volumes used for training SynEM. Synaptic labels were made for 115 synapses (note that the training set in The approach of 3) with a border of 280 nm around each tile. After classification, the borders were discarded, and tiles were stitched together. The classifier output was thresholded and morphologically closed with a cubic structuring element of three voxels edge length. Then, connected components of the thresholded classifier output with a size of at least 50 voxels were identified. Synapse detection precision and recall rates were determined as follows: A ground truth synapse  was considered detected (TP) if it had at least a single voxel overlap with a predicted component. A ground truth synapse was counted as a false negative detection if it did not overlap with any predicted component (FN). To determine false positive classifications, we evaluated the center of the test volume (shrunk by 160 nm from each side to 484 × 484 × 246 voxels) and counted each predicted component that did not overlap with any of the ground truth synapses as false positive detection (FP). For this last step, we used all ground truth synapses from the initial test set, in favor of the For voxel classification all features proposed in  and 200 3 that were centered in five of the interface training set cubes different from the one used for voxel prediction resulting in 299 labels . All object features provided by Ilastik were used for object classification. The evaluation on the test set was done as for  trained on mitochondria, vesicle clouds and membranes. The membrane predictions were discarded and the vesicle clouds and mitochondria labels were first proofread by undergraduate students and then twice by an expert neuroscientist. The voxels labels for synaptic junctions were added by an expert neuroscientist based on the identified synapses in the interface training data. Overall 310 synapses were annotated in the training volume. A recursive multi-class CNN was trained on this data with the same architecture and hyperparameter settings as described in (For comparison with  six of tribed in  using thribed in  with a chttps://github.com/neurodata-arxiv/CAJAL). The segmentation in the bounding box  (resolution 1) was adapted to have a one-voxel boundary between segments by first morphologically eroding the original segmentation with a 3-voxel cubic structuring element and running the MATLAB watershed function on the distance-transform of the eroded segmentation on a tiling with cubes of size  voxels. Since the The image data, neurite and synapse segmentation from  hosted os on the test set:The neuron-to-neuron connection recall was calculated assuming an empirical distribution p(n) of the number of synapses n between connected excitatory neurons given by published studies see . For inhnn is the threshold on the number of synapses between a neuron pair to consider it as connected (see nn = P(k ≥ γnn | Rs).Where γcted see . This eqs) made by a classifier with precision Ps and recall Rs:To compute the neuron-to-neuron precision, we first calculated the expected number of false positive synapse detections .We then used a Poisson distribution to estimate the number of cases in which at least γnn are given in terms of the neuron-to-neuron connection recall Rnn byFinally, the true positive detections of neuron-to-neuron connections in the connectome TPnn is given byTogether, the neuron-to-neuron connection precision Pr = 0.2 (The connectivity ratio was set to cr = 0.2  for excir = 0.2 .w (nn (bin (nn = 1 for excitatory and γnn = 2 for inhibitory connections (s) as synaptic. To obtain the final synapse count the retrieved synaptic interfaces were clustered using hierarchical clustering with single linkage and a distance cutoff between the centroids of the interfaces of 320.12 nm (this distance cutoff was obtained by optimizing the synapse density prediction on the test set).For determining the local connectome  between w , we applw  all spine synapses in the test set were considered for which SynEM had detected at least one overlapping neurite interface  could represent a substantial amount of work. We suggest that you consider consolidating points 6 and 7 in reviewer #2's comments and compare the performance between SynEM, Dorkenwald et al., 2017, Roncal et al., 2014, and Becker et al., 2012 to at least one additional 3D EM dataset . The results would help to clarify SynEM's performance and applicability. You should consider reviewer #2's point 8 as optional.In sum, we appreciate that the reviewers' comments cover a broad range of suggestions for improving the manuscript. Please use your best judgment in deciding which of these can be accommodated in a reasonable period of time.Reviewer #1:The manuscript by Staffler et al. describes a method for automatically detecting synapses in electron microscopy images collected using the block face scanning microscope. The authors report a high precision of 97%.The appearance of this work is timely. An algorithm that is able to accurately detect synapses in electron micrographs with a lower z resolution than focussed ion beam images will be of interest to a number of groups. As the authors state, the method is scalable for larger sets of images, so should have a broad appeal.This work is well presented and gives a thorough evaluation of the algorithms performance, as well as the comparison with other methods.I have only a few relatively minor comments about the content of the paper. However, my major reservation concerns its suitability for this type of journal. The method is clearly tested on a very specific set of images, and its performance appears impressive, but beyond this there is little insight about the revealed connectivity. This is clearly a technically important piece of work, but I wonder if it should not be suited to a more specialist journal.On a more specific note, I was confused about the testing of the performance on the different types of synapses. There is a description of how synapses along inhibitory axons were identified, and the success was high. However, does this mean that it is able to distinguish the contacts, but not classify between excitatory and inhibitory? How does the overall success for identifying synapses in the entire volume account for the two types of synapse? I presume from the description that it is simply identifying a contact, and no distinction is made. This needs to be clarified. It is perhaps a minor point, but nevertheless it is not explicitly given. It is also not clear as to whether the program is able to distinguish those contacts that are found on the dendritic shaft as opposed to those on the spine. Contextual information is being used from these different sites of contact, but are they given in the final result?Reviewer #2:Staffler and colleagues present SynEM, a tool for automated chemical synapse inference in volumetric electron microscopy data of cortical neuropil. The authors report SynEM: (1) provides 97% precision and recall of binary cortical connectivity without user interaction, (2) scales to large volumes, and (3) possibly to whole-brain datasets. SynEM is a natural extension of the group's analysis workflow for connectomics. Relying on volumetric neurite segmentation following manual skeletonization with SegEM , SynEM performs synapse inference by classifying interfaces between neurites based on spatial, texture, and shape features. The authors apply SynEM to their SBEM dataset from the mouse cortex and present the extracted connectivity as adjacencies for a local connectome in L4 of barrel cortex.SynEM could be of potential useful to the field, however, there are several important concerns about its potential presented here:3 , in the authors' 86 x 86 x 52 μm3 volume (1) Is SBEM + SegEM + SynEM comprehensively detecting structural connectivity? This is a fundamental advantage of EM. Estimating 1 synapse/μm3 volume , one wouIf (A), please clarify in the text/methods and let readers know if the synapse density is as expected, or why not. Given that the segmentation with SegEM was volumetric, a comprehensive connectome within the volume should be possible. If not, why did the authors choose only 104 axons and 100 postsynaptic processes?If (B), please explain why neuropil in this dataset is not representative.If (C) or (D), this is, of course, would significantly limit the utility of these approaches for the field as the results would not accurately represent the structural connectivity of neuronal networks.2) Ground truth: synapse numbers were generated by viewing post-segmentation post-border detection volumes. How many synapses were lost during these preprocessing steps? A comparison against ground-truth synapse data generated using an independent method is necessary to judge the overall accuracy of the method in detecting synapses within a volume .2) compared to previous reports. PSD areas have reported median values of < 0.05 μm2 between hippocampal pyramidal cells  and appear to be even smaller for both putative thalamic and non-thalamic synapses in L4 of mouse barrel cortex . Thus, synapses < 0.1 μm2 are unlikely to be a small fraction of the actual population as asserted . Rather, it is worrisome that this approach may be missing synapses detectable with other methods (points 1-2), leaving and enriching for synapses easily detected by SynEM.3) Are precision and recall rates elevated by large synapses? The distribution of synapse sizes reported in the Supplemental Synapse Gallery (subsection “SynEM workflow and training data”) appears heavily weighted toward larger interfaces (median > 0.1 μm4) This reader found it difficult to interpret how ground-truth neuron-to-neuron connections were generated. It appears that neuron-to-neuron connectivity precision was calculated using several values taken from the literature . An additional assumption in their pairwise connectivity model is that connectivity is random and independent. There is increasing evidence that this assumption is not supported in the rodent cortex . Is the cost of ground-truthing the reason these numbers were not measured from the dataset used to evaluate SynEM? This is an important value to get correct given the authors' claims.5) SegEM errors influencing SynEM: SynEM requires a segmented volume (generated here by SegEM). A fuller description of how segmentation errors affect SynEM performance (more than just a few example images) would allow the reader to determine what segmentation results are suitable for application of SynEM . Description of how errors in assignment of extracellular space affect SynEM performance would allow the authors support their claim that space-preserving EM preparations would simplify synapse detection.6) Comparisons and references to relevant and available state-of-the-art tools: To provide evidence that SynEM provides a substantial methodological advance with the new potential to facilitate difficult or intractable experiments, the authors should provide fair comparisons of SynEM to multiple the state-of-the-art approaches. The authors do well to identify a set , but appDorkenwald, S. et al. Automated synaptic connectivity inference for volume electron microscopy. Nat Methods (2017).Perez, A.J. et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. (2014).https://arxiv. org/abs/1403.3724 (2014).Roncal, W.G. et al. VESICLE: volumetric evaluation of synaptic interfaces using computer vision at large scale. Preprint available at Dorkenwald, 2017 was published just recently so its omission is perhaps unsurprising; however, Dorkenwald, 2017 and Roncal, 2014 are particularly important with a similar aims of large-scale synapse inference and may provide competitive or better performance compared to SynEM than the other approaches the authors list.The authors should also show the precision-recall curves for the other methods (perhaps the 3 best performers from 7) Generalizability: A major impediment to the EM connectomics field has been that analysis workflows have been highly specific to particular dataset types. It would be of substantially more value if SynEM's utility is demonstrated across different 3D EM dataset types. The abstract states: "we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks"https://neurodata.io/data/kasthuri15), ssTEM (eg. https://neurodata.io/data/bock11), FIB-SEM (e.g. http://cvlab.epfl.ch/data/em), and other SBEM data. Alternatively, the authors could modify their description of scope, narrowing the utility of their approach to SBEM. In this case, one would, still want to see the workflow evaluated on at least one other independent dataset.As the authors are aware, there are multiple approaches to generate 3D EM datasets. To provide evidence for the utility across 3D EM image stacks, the authors should apply SynEM to other EM datasets. Ideally, datasets from 3D EM methods  other than SBEM. This approach (1) would demonstrate generalizability; (2) could provide a better understanding of dataset parameters influencing SynEM and automated segmentation performance more broadly ; and (3) benefits from the authors' knowledge on how to best tune the SynEM pipeline as compared to others' workflows . This should be straightforward. Several public datasets exist of ATUM-SEM (e.g. 8) Directly test the claim of lower resolution performance: The authors propose that better segmentation tools are needed for higher-imaging throughput, lower-resolution datasets and that SynEM's performance is particularly useful for such data. In addition to testing performance on other dataset types (point 7), it should be straightforward for the authors to compare performance on their 6 nm data . The aut3 of cortex, approaches must be developed to accurately detect billions of synapses. In the abstract, the authors also assert that SynEM may plausibly scale to whole-brain datasets. It is unclear synEM the tool up to this task. As alluded to in (point 1) above, the synapse detection rates are surprisingly low. Moreover, there is little detail about how the approach scales to much larger volumes in terms of compute time and resources or other aspects of effort in the pipeline. Thus, it is difficult for this reviewer to estimate the cost and effort of implementing this workflow at such scales.9) Scalability: The authors strongly pitch that to analyze a 1 mm10) Terminology: binary vs. binary and undirected. To this reviewer, the use of 'binary' should be clarified and made consistent throughout the text. At the synapse level, the authors use binary to classify appositions as synapses or not, irrespective of direction.Subsection “SynEM workflow and training data”. The SynEM score was then thresholded to obtain an automated binary classification of interfaces into synaptic / non-synaptic θ in .Subsection “SynEM workflow and training data” and Whereas, at the connection level  binary is used for whether or not there is at least γ synapses. These are, however, are directed connections.Subsection “SynEM for connectomes”.We assume that the goal is a binary connectome containing the information whether pairs of neurons are connected or not.Subsection “SynEM for connectomes”. “binary connectomes by considering all neuron pairs with at least γnn synapses as connected”Convention in the field is for 'binary' to be used for unweighted connectivity, not to describe an undirected synapse. In descriptions of connectivity this reviewer would suggest using 'undirected-binary', 'directed-binary', 'undirected-weighted', or 'directed-weighted' not just 'binary' across the text. Reviewer #1:I have only a few relatively minor comments about the content of the paper. However, my major reservation concerns its suitability for this type of journal. The method is clearly tested on a very specific set of images, and its performance appears impressive, but beyond this there is little insight about the revealed connectivity. This is clearly a technically important piece of work, but I wonder if it should not be suited to a more specialist journal.We addressed the potential concern of a “specific set of images”, also following the suggestions by reviewer 2, and tested SynEM on a different 3D EM dataset obtained using ATUM-SEM . SynEM also outperforms the state-of-the art method on this dataset  between neurites. It then classifies the contact as synaptic or not, irrespective of synapse type .It should also be noted that the comparison to published methods for synapse detection is reported for all synapses , see Reviewer #2:3 , in the authors' 86 x 86 x 52 μm3 volume ([…] 1) Is SBEM + SegEM + SynEM comprehensively detecting structural connectivity? This is a fundamental advantage of EM. Estimating 1 synapse/μm3 volume , one wouIf (A), please clarify in the text/methods and let readers know if the synapse density is as expected, or why not. Given that the segmentation with SegEM was volumetric, a comprehensive connectome within the volume should be possible. If not, why did the authors choose only 104 axons and 100 postsynaptic processes?If (B), please explain why neuropil in this dataset is not representative.If (C) or (D), this is, of course, would significantly limit the utility of these approaches for the field as the results would not accurately represent the structural connectivity of neuronal networks.The reviewer addresses an important point: the exhaustive detection of synapses. Our local connectome reported as an example in 3), yielding 195.644 synapse detections, in fact very close to the reviewers’ estimate. We have added this result to the main text to help clarify this issue also for other readers .To address the specific concern of the reviewer, we have run SynEM on half of the entire cortex dataset (volume: 192296 µm2) Ground truth: synapse numbers were generated by viewing post-segmentation post-border detection volumes. How many synapses were lost during these preprocessing steps? A comparison against ground-truth synapse data generated using an independent method is necessary to judge the overall accuracy of the method in detecting synapses within a volume .For training data generation, the described more efficient tool was used (subsection “SynEM workflow and training data”). For test set generation, however, the test volume was meticulously volume searched for synapses using only our 3D data viewer webKnossos . Therefore our test results  constitute such an independent method of expert synapse detection .2) compared to previous reports. PSD areas have reported median values of < 0.05 μm2 between hippocampal pyramidal cells  and appear to be even smaller for both putative thalamic and non-thalamic synapses in L4 of mouse barrel cortex . Thus, synapses < 0.1 μm2 are unlikely to be a small fraction of the actual population as asserted . Rather, it is worrisome that this approach may be missing synapses detectable with other methods (points 1-2), leaving and enriching for synapses easily detected by SynEM.3) Are precision and recall rates elevated by large synapses? The distribution of synapse sizes reported in the Supplemental Synapse Gallery (subsection “SynEM workflow and training data”) appears heavily weighted toward larger interfaces  area, not the PSD area, that the reviewer is referring to. We acknowledge the reviewer’s concern about the dependence between synapse size and automated detection. In the synapse gallery, we reported the As to axon-spine interfaces, we report these in new Together it is evident that our detection is not biased towards larger synapses than previously reported.4) This reader found it difficult to interpret how ground-truth neuron-to-neuron connections were generated. It appears that neuron-to-neuron connectivity precision was calculated using several values taken from the literature . An additional assumption in their pairwise connectivity model is that connectivity is random and independent. There is increasing evidence that this assumption is not supported in the rodent cortex . Is the cost of ground-truthing the reason these numbers were not measured from the dataset used to evaluate SynEM? This is an important value to get correct given the authors' claims.The reviewer discusses our estimates of SynEM performance for neuron-to- neuron connections old . The rev5) SegEM errors influencing SynEM: SynEM requires a segmented volume (generated here by SegEM). A fuller description of how segmentation errors affect SynEM performance (more than just a few example images) would allow the reader to determine what segmentation results are suitable for application of SynEM . Description of how errors in assignment of extracellular space affect SynEM performance would allow the authors support their claim that space-preserving EM preparations would simplify synapse detection.The reviewer raises a relevant point. We use a published SegEM-segmentation , whose properties are described and quantified there at great detail, including downloadable datasets, allowing a comparison to other segmentation approaches.To address the particular question of whether the quality of SegEM affects SynEM performance, we locally corrected all SynEM classification errors (FPs and FNs) of the test set and reran the SynEM classification. In fact, 13 of 28 FNs were caused by over-split SegEM segments, similarly 9 of 27 FPs. This means that the remaining error rate of SynEM could be even smaller when a “perfect” volume segmentation can be used. We added this point to the manuscript .6) Comparisons and references to relevant and available state-of-the-art tools: To provide evidence that SynEM provides a substantial methodological advance with the new potential to facilitate difficult or intractable experiments, the authors should provide fair comparisons of SynEM to multiple the state-of-the-art approaches. The authors do well to identify a set , but appDorkenwald, S. et al. Automated synaptic connectivity inference for volume electron microscopy. Nat Methods (2017).Perez, A.J. et al. A workflow for the automatic segmentation of organelles in electron microscopy image stacks. Front. Neuroanat. (2014).https://arxiv. org/abs/1403.3724 (2014).Roncal, W.G. et al. VESICLE: volumetric evaluation of synaptic interfaces using computer vision at large scale. Preprint available at Dorkenwald, 2017 was published just recently so its omission is perhaps unsurprising; however, Dorkenwald, 2017 and Roncal, 2014 are particularly important with a similar aims of large-scale synapse inference and may provide competitive or better performance compared to SynEM than the other approaches the authors list.The authors should also show the precision-recall curves for the other methods , and only implemented full- scale comparisons for those methods that were best performing at the time . This is documented in However, to address this issue, we have now added a comparison to Dorkenwald, 2017, which is itself better than any other published methods . Please find the results in new 7) Generalizability: A major impediment to the EM connectomics field has been that analysis workflows have been highly specific to particular dataset types. It would be of substantially more value if SynEM's utility is demonstrated across different 3D EM dataset types. The abstract states: "we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks"https://neurodata.io/data/kasthuri15), ssTEM (eg. https://neurodata.io/data/bock11), FIB-SEM (e.g. http://cvlab.epfl.ch/data/em), and other SBEM data. Alternatively, the authors could modify their description of scope, narrowing the utility of their approach to SBEM. In this case, one would, still want to see the workflow evaluated on at least one other independent dataset.As the authors are aware, there are multiple approaches to generate 3D EM datasets. To provide evidence for the utility across 3D EM image stacks, the authors should apply SynEM to other EM datasets. Ideally, datasets from 3D EM methods  other than SBEM. This approach (1) would demonstrate generalizability; (2) could provide a better understanding of dataset parameters influencing SynEM and automated segmentation performance more broadly ; and (3) benefits from the authors' knowledge on how to best tune the SynEM pipeline as compared to others' workflows . This should be straightforward. Several public datasets exist of ATUM-SEM  – but this is a strategic discussion beyond the scope of the manuscript.To address the concern about generalizability to other 3D EM data, we followed the reviewer’s suggestion and applied SynEM to the ATUM-SEM data by Kasthuri et al., 2015. Please find the results in With this, we hope to have addressed the concerns in points 6 and 7 about comparison to the latest best performing tool  and for other 3D EM data.8) Directly test the claim of lower resolution performance: The authors propose that better segmentation tools are needed for higher-imaging throughput, lower-resolution datasets and that SynEM's performance is particularly useful for such data. In addition to testing performance on other dataset types (point 7), it should be straightforward for the authors to compare performance on their 6 nm data . The autWe agree that this would be an interesting extension, and in fact we do think that a bit lower resolution is still sufficient for automated synapse detection – but we would like to follow the editors’ judgement that this is not a required point for the revision.3 of cortex, approaches must be developed to accurately detect billions of synapses. In the abstract, the authors also assert that SynEM may plausibly scale to whole-brain datasets. It is unclear synEM the tool up to this task. As alluded to in (point 1) above, the synapse detection rates are surprisingly low. Moreover, there is little detail about how the approach scales to much larger volumes in terms of compute time and resources or other aspects of effort in the pipeline. Thus, it is difficult for this reviewer to estimate the cost and effort of implementing this workflow at such scales.9) Scalability: The authors strongly pitch that to analyze a 1 mmOf course SynEM has a very high recall as reported  – and de3 dataset would run about 280 days on a mid-size computational cluster (extrapolated from 2.5 days run time for the cortex dataset reported here). This is well comparable to segmentation run time, and much less than the still required human interaction time. We added these estimates to the manuscript .As to the computational load: SynEM has not been optimized yet for speed. But even with the current matlab code, synapse detection on a 1 mm10) Terminology: binary vs. binary and undirected. To this reviewer, the use of 'binary' should be clarified and made consistent throughout the text. At the synapse level, the authors use binary to classify appositions as synapses or not, irrespective of direction.Subsection “SynEM workflow and training data”. The SynEM score was then thresholded to obtain an automated binary classification of interfaces into synaptic / non-synaptic θ in .Subsection “SynEM workflow and training data” and Whereas, at the connection level  binary is used for whether or not there is at least γ synapses. These are, however, are directed connections.Subsection “SynEM for connectomes”. We assume that the goal is a binary connectome containing the information whether pairs of neurons are connected or not.Subsection “SynEM for connectomes”.“binary connectomes by considering all neuron pairs with at least γnn synapses as connected”Convention in the field is for 'binary' to be used for unweighted connectivity, not to describe an undirected synapse. In descriptions of connectivity this reviewer would suggest using 'undirected-binary', 'directed-binary', 'undirected-weighted', or 'directed-weighted' not just 'binary' across the text.We appreciate this point by the reviewer and have amended the terminology accordingly (subsection “SynEM evaluation”, subsection “SynEM classifier training and validation” and