BOL: Related items

MafTools

Jit — Thu, 16 Feb 2017 11:16:01 -0600

maftools - An R package to summarize, analyze and visualize MAF files. Introduction.

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widley accepted and used to store variants detected. The Cancer Genome Atlas Project has seqenced over 30 different cancers with sample size of each cancer type being over 200. The resulting data consisting of genetic variants is stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner either from TCGA sources or any in-house studies as long as the data is in MAF format. Maftools can also handle ICGC Simple Somatic Mutation format.

maftools is on bioRxiv

Please cite the below if you find this tool useful for you.

Mayakonda, A. and H.P. Koeffler, Maftools: Efficient analysis, visualization and summarization of MAF files from large-scale cohort based cancer studies. bioRxiv, 2016. doi: http://dx.doi.org/10.1101/052662

Address of the bookmark: https://github.com/PoisonAlien/maftools

ECTOOLS: Long Read Correction and other Correction tools

Jit — Fri, 05 Jan 2018 04:02:22 -0600

Long Read Correction and other Correction tools

This package is a loose collection of scripts. To run the correction
routine see the section below. Descriptions of the other scripts
are at the bottom of this file.

Contact: gurtowsk@cshl.edu

In short, the correction algorithm takes as input the unitigs from a short read assembly and uses them to correct long read data. More background information for the algorithm can be found:
http://schatzlab.cshl.edu/presentations/2013-06-18.PBUserMeeting.pdf

Address of the bookmark: https://github.com/jgurtowski/ectools

PEAR: a fast and accurate Illumina Paired-End reAd mergeR

Jit — Wed, 06 Apr 2016 13:27:23 -0500

PEAR is an ultrafast, memory-efficient and highly accurate pair-end read merger. It is fully parallelized and can run with as low as just a few kilobytes of memory.

PEAR evaluates all possible paired-end read overlaps and without requiring the target fragment size as input. In addition, it implements a statistical test for minimizing false-positive results. Together with a highly optimized implementation, it can merge millions of paired end reads within a couple of minutes on a standard desktop computer.

More at http://www.exelixis-lab.org/web/software/pear

Paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3933873/

Address of the bookmark: http://www.exelixis-lab.org/web/software/pear

LAMSA: fast split read alignment with long approximate matches

Jit — Tue, 15 May 2018 04:44:42 -0500

LAMSA (Long Approximate Matches-based Split Aligner) is a novel split alignment approach with faster speed and good ability of handling SV events. It is well-suited to align long reads (over thousands of base-pairs). LAMSA takes takes the advantage of the rareness of SVs to implement a specifically designed two-step strategy. That is, LAMSA initially splits the read into relatively long fragments and co-linearly align them to solve the small variations or sequencing errors, and mitigate the effect of repeats. The alignments of the fragments are then used for implementing a sparse dynamic programming (SDP)-based split alignment approach to handle the large or non-co-linear variants. We benchmarked LAMSA with simulated and real datasets having various read lengths and sequencing error rates, the results demonstrate that it is substantially faster than the state-of-the-art long read aligners; mean-while, it also has good ability to handle various categories of SVs. LAMSA is open source and free for non-commercial use. LAMSA is mainly designed by Bo Liu & Yan Gao and developed by Yan Gao in Center for Bioinformatics, Harbin Institute of Technology, China.

Address of the bookmark: https://github.com/hitbc/LAMSA

nanofilt: Filtering and trimming of long read sequencing data

Jit — Mon, 30 Jul 2018 12:01:52 -0500

Filtering on quality and/or read length, and optional trimming after passing filters.
Reads from stdin, writes to stdout.

Intended to be used:

directly after fastq extraction
prior to mapping
in a stream between extraction and mapping

https://github.com/wdecoster/nanofilt

Address of the bookmark: https://github.com/wdecoster/nanofilt

BamView: a free interactive display of read alignments in BAM data files

Neel — Fri, 09 Nov 2018 13:43:22 -0600

To run the application on UNIX from the downloaded jar file run the UNIX:

java -mx512m -jar BamView.jar

and extra command line options are given when '-h' is used:

java -jar BamView.jar -h

BAM files can be specified on the command line with the '-a' option:

java -mx512m -jar BamView.jar -a pathToFile/sorted.bam

If a BAM filename is not given on the command line BamView will prompt for a file to be entered. The BAM index file should have the same name as the BAM file but with a '.bai' suffix. Multiple BAM files can be loaded and overlaid in the viewer. To make this easier BamView will read in files that contain a list of filenames.

Address of the bookmark: http://bamview.sourceforge.net/

Alvis: a tool for contig and read ALignment VISualisation and chimera detection

LEGE — Wed, 08 May 2024 07:02:55 -0500

Alvis, a simple command line tool that can generate visualisations for a number of common alignment analysis tasks. Alvis is a fast and portable tool that accepts input in a variety of alignment formats and will output production ready vector images. Additionally, Alvis will highlight potentially chimeric reads or contigs, a common source of misassemblies.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04056-0

Address of the bookmark: https://github.com/SR-Martin/alvis

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Jit — Thu, 19 Apr 2018 08:06:03 -0500

Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).

Address of the bookmark: https://github.com/meiji-bioinf/heap

MMseqs2: ultra fast and sensitive sequence search and clustering suite

Abhi — Wed, 06 Oct 2021 07:01:14 -0500

MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Address of the bookmark: https://github.com/soedinglab/MMseqs2