<commit_before># bioscripts - scripts I am using on a daily basis

# Fastq,bam,cram

* bam2fq.sh converts bam to fastq, uses samtools and bedtools
* bam.reads_number.sh reports the number of paired reads in a bam file, uses samtools
* bam.remove_region.sh removes reads from a bam file located at regions specified by a bed file.
Badly filtered rRNA-depleted RNA-seq samples may have huge coverage of low complexity regions. 
It is better to filter those with prinseq, however sometimes it is necessary to remove a particular region.
* cram2fq.sh converts cram to fastq, uses cramtools wrapper from bcbio

# RNA-seq

* rnaseq.star.sh - 2pass on the fly STAR alignment for a single sample
* [rnaseq.feature_counts.sh](../master/rnaseq.feature_counts.sh) [file.bam] calculates features (reads) for RPKM calculation in R, outputs length of the genes. TPMs are generally better
(http://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/), because you can compare expression values across samples, and they are calculated by default by bcbio rnaseq pipeline, 
but GTEX values are in RPKMs, and still many people think in terms of RPKMs.

### Differential expression

* dexpression.katie.R - edgeR DE, batch effect correction, pheatmap, GO, pathways

### Splicing analysis
<commit_msg>	modified:   README.md<commit_after># bioscripts - scripts I am using on a daily basis

# Fastq,bam,cram

* bam2fq.sh converts bam to fastq, uses samtools and bedtools
* bam.reads_number.sh reports the number of paired reads in a bam file, uses samtools
* bam.remove_region.sh removes reads from a bam file located at regions specified by a bed file.
Badly filtered rRNA-depleted RNA-seq samples may have huge coverage of low complexity regions. 
It is better to filter those with prinseq, however sometimes it is necessary to remove a particular region.
* cram2fq.sh converts cram to fastq, uses cramtools wrapper from bcbio

# RNA-seq

* rnaseq.star.sh - 2pass on the fly STAR alignment for a single sample
* [rnaseq.feature_counts.sh](../master/rnaseq.feature_counts.sh) [file.bam] calculates features (reads) for RPKM calculation in R, outputs length of the genes. TPMs are generally better
(http://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/), because you can compare expression values across samples, and they are calculated by default in bcbio rnaseq pipeline, 
but [http://www.gtexportal.org](GTEX) values unlike [http://www.proteinatlas.org/)(Protein atlas) values are in RPKMs, and still many people think in terms of RPKMs. GTEX has much more samples than HPA.

### Differential expression

* dexpression.katie.R - edgeR DE, batch effect correction, pheatmap, GO, pathways

### Splicing analysis

{'fraction_non_alphanumeric': 0.07245841035120149, 'fraction_numerical': 0.0022181146025878, 'mean_word_length': 4.5112016293279025, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 4, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1130290', 'n_tokens_mistral': 854, 'n_tokens_neox': 816, 'n_words': 347}