Tag: RSeQC

RSeQC TIN calc – [NOTE:input bed must be 12-column] skipped this line:

Ran tin.py in Rseqc, and I got all of the outputs with TIN calculations, but when looking at the error files, many of the lines say “[NOTE:input bed must be 12-column] skipped this line:”. I converted gtf from Ensembl to bed file, and this is the general layout: MT 15725…

Continue Reading RSeQC TIN calc – [NOTE:input bed must be 12-column] skipped this line:

Nextflow rnaseq finishing early

Nextflow rnaseq finishing early 0 Hi I’m running the RNA-seq pipeline from nextflow and I have been running it without problems until this dataset it just stops prematurely saying it has finished when it doesn’t even aligns the reads with salmon. Any ideas what may be going on? I have…

Continue Reading Nextflow rnaseq finishing early

Transcript feature coverage with coverage of each feature shown like e.g. UTR’s CDS, exons etc

Transcript feature coverage with coverage of each feature shown like e.g. UTR’s CDS, exons etc 0 Hi, We are all familiar with rseqc genebody coverage functions rseqc.sourceforge.net/#genebody-coverage-py. But I was wondering if there is an existing implementation of transcript level coverage with each of the features of transcript (like 5’UTR,…

Continue Reading Transcript feature coverage with coverage of each feature shown like e.g. UTR’s CDS, exons etc

How to create Dockerfile without copying large data input and Build image such that snakemake wokflow run as Entrypoint

How to create Dockerfile without copying large data input and Build image such that snakemake wokflow run as Entrypoint 1 I have project folder structure like Below : which has size of more than 50 GB . When i am creating Dockerfile such that Snakefile workflow which utilizes data from…

Continue Reading How to create Dockerfile without copying large data input and Build image such that snakemake wokflow run as Entrypoint

10 BAM (Bioinformatics Alignment/Map) Best Practices

Bioinformatics Alignment/Map (BAM) is a powerful tool used to analyze and compare biological sequences. BAM is used to identify genetic variations, detect structural rearrangements, and compare different genomes. It is an essential tool for many areas of bioinformatics, including genomics, proteomics, and transcriptomics. In this article, we will discuss 10…

Continue Reading 10 BAM (Bioinformatics Alignment/Map) Best Practices

Population-level variation in enhancer expression identifies disease mechanisms in the human brain

Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). PubMed Central  Article  CAS  Google Scholar  Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017). CAS  PubMed  PubMed Central …

Continue Reading Population-level variation in enhancer expression identifies disease mechanisms in the human brain

Low transcript quantification with Salmon using GRCm39 annotations

Hi everyone, first time working with mouse samples and unfortunately, there are fewer resources available for the latest mouse Ensembl genome than I was expecting. What I’ve done: I performed rRNA depletion on total RNA extracted from mouse tissue and created Illumina libraries using a cDNA synthesis kit with random…

Continue Reading Low transcript quantification with Salmon using GRCm39 annotations

[MonashBioinformaticsPlatform/RSeQC] junction_saturation not suit for bam/sam file generated by minimap or pbmm2

because the CIGAR in bam/sam file generated by minimap2 contain “=” , represent right match with reference, and “X”, represent wrong match with reference. while the bam_cigar.py in ./lib/qcmodule/bam_cigar.py only suit for bam/sam generated such as BWA/bowtie, which CIGAR contain only “M” ,represent mis/match. So i modified the bam_cigar.py 77…

Continue Reading [MonashBioinformaticsPlatform/RSeQC] junction_saturation not suit for bam/sam file generated by minimap or pbmm2

Highly mapped to introns

Highly mapped to introns 0 Hi, I am analyzing RNA-seq data from human blood samples. I checked the read distribution using RSeQC read_distribution after mapping by STAR. Usually, I get more than 80% of reads mapped to exons. However, at this time, the result showed only several % were mapped…

Continue Reading Highly mapped to introns

Interpreting read coverage over gene body plot

Interpreting read coverage over gene body plot 0 Hi, I’m working on some RNA-seq data for my thesis and I was hoping that someone could help me out. My sequencing library was prepared using Illumina TruSeq Stranded mRNA kit and sequenced with a NovaSeq sequencer. After read alignment I did…

Continue Reading Interpreting read coverage over gene body plot