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Comments- Neel 2197 days ago
Using KAT again (You will need the modules:
KAT/2.1.1andgnuplot/4.6.5) – we can plot the kmer content of the assembly compared to the kmer content of the read set. The first thing we need to do is to combine the reads into a single file, for gzipped files, this can be done withzcat, or for unzipped filescat.Ex.
$ cat reads_R1.fastq >> combined.fastq $ zcat reads_R2.fastq.gz >> combined.fastqWe will now use
kat compto create a kmer content comparison. Usekat comp --helpto get help for the program, then create a comparison between the combined reads and the assembly. Make sure that you use the flags for canonical hashes for both sequence 1 and 2, as well as 8 threads. Finally, clean up you working directory by removing the combined fasta file, and re-zipping any unzipped files. Then download the output files to you computer usingscpand look at the png file that was produced.- Does the kmer content look good to you?
- How much of the kmer “noise” is part of the final assembly?
- What do you think contamination would look like in the kmer plot?
- What can the kmer graph tell you about the ploidy of the organism?
- Rahul Nayak published a blog post Installing Salmon for Trinity !Comments
- Rahul Nayak 2988 days ago
Sometime, you need to provide the path
➜ Tools git:(master) ✗ PATH=$PATH:/home/tools/anaconda3/bin
➜ Tools git:(master) ✗ export $PATH
- Jit bookmarked RNA-seq Analysis Workshop Course Materials
- Rahul Nayak posted to the wire
- Rahul Nayak created a new bio-script Perl subroutine to read genome/reads fasta file !
- Rahul Nayak published a blog post Installing Trinity !
- Sanjay is now a friend with Aaryan Lokwani
- Aaryan Lokwani is now a friend with Sanjay
- Jit created a new bio-script Bash script to run Busco2 !
- Jit commented on a bookmark AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
- Jit bookmarked AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq dataComments
- Jit 2998 days ago
- AfterQC AfterQC - Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data.
- dupRadar dupRadar. An R package which provides functions for plotting and analyzing the duplication rates dependent on the expression levels.
- FastQC FastQC is a quality control tool for high-throughput sequence data (Babraham Institute) and is developed in Java. Import of data is possible from FastQfiles, BAM or SAM format. This tool provides an overview to inform about problematic areas, summary graphs and tables to rapid assessment of data. Results are presented in HTML permanent reports. FastQC can be run as a stand-alone application or it can be integrated into a larger pipeline solution.
- fastqp fastqp. Simple FASTQ quality assessment using Python.
- Kraken kraken:A set of tools for quality control and analysis of high-throughput sequence data.
- HTSeq HTSeq.The Python script htseq-qa takes a file with sequencing reads (either raw or aligned reads) and produces a PDF file with useful plots to assess the technical quality of a run.
- mRIN mRIN - Assessing mRNA integrity directly from RNA-Seq data.
- MultiQC MultiQC- Aggregate and visualise results from numerous tools (FastQC, HTSeq, RSeQC, Tophat, STAR, others..) across all samples into a single report.
- NGSQC NGSQC: cross-platform quality analysis pipeline for deep sequencing data.
- NGS QC Toolkit NGS QC Toolkit A toolkit for the quality control (QC) of next generation sequencing (NGS) data. The toolkit comprises user-friendly stand alone tools for quality control of the sequence data generated using Illumina and Roche 454 platforms with detailed results in the form of tables and graphs, and filtering of high-quality sequence data. It also includes few other tools, which are helpful in NGS data quality control and analysis.
- PRINSEQ PRINSEQ is a tool that generates summary statistics of sequence and quality data and that is used to filter, reformat and trim next-generation sequence data. It is particular designed for 454/Roche data, but can also be used for other types of sequence.
- QC-Chain QC-Chain is a package of quality control tools for next generation sequencing (NGS) data, consisting of both raw reads quality evaluation and de novo contamination screening, which could identify all possible contamination sequences.
- QC3 QC3 a quality control tool designed for DNA sequencing data for raw data, alignment, and variant calling.
- qrqc qrqc. Quickly scans reads and gathers statistics on base and quality frequencies, read length, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected.
- RNA-SeQC RNA-SeQCis a tool with application in experiment design, process optimization and quality control before computational analysis. Essentially, provides three types of quality control: read counts (such as duplicate reads, mapped reads and mapped unique reads, rRNA reads, transcript-annotated reads, strand specificity), coverage (like mean coverage, mean coefficient of variation, 5’/3’ coverage, gaps in coverage, GC bias) and expression correlation (the tool provides RPKM-based estimation of expression levels). RNA-SeQC is implemented in Java and is not required installation, however can be run using the GenePattern web interface. The input could be one or more BAM files. HTML reports are generated as output.
- RSeQC RSeQC analyzes diverse aspects of RNA-Seq experiments: sequence quality, sequencing depth, strand specificity, GC bias, read distribution over the genome structure and coverage uniformity. The input can be SAM, BAM, FASTA, BED files or Chromosome size file (two-column, plain text file). Visualization can be performed by genome browsers like UCSC, IGB and IGV. However, R scripts can also be used to visualization.
- SAMStat SAMStat identifies problems and reports several statistics at different phases of the process. This tool evaluates unmapped, poorly and accurately mapped sequences independently to infer possible causes of poor mapping.
- SolexaQA SolexaQA calculates sequence quality statistics and creates visual representations of data quality for second-generation sequencing data. Originally developed for the Illumina system (historically known as “Solexa”), SolexaQA now also supports Ion Torrent and 454 data.
- Trim galore! Trim_galore is a wrapper script to automate quality and adapter trimming as well as quality control, with some added functionality to remove biased methylation positions for RRBS sequence files (for directional, non-directional (or paired-end) sequencing).
- Rahul Nayak posted to the wire
