BOL: Related items

LSC: Improving PacBio Long Read Accuracy by Short Read Alignment

Abhimanyu Singh — Thu, 06 Sep 2018 16:27:35 -0500

Added Command line argument support.
Multi-stage execution modes.
Support for parallelization. Now execution proceeds in batches of long reads the size of which can be set by --long_read_batch_size N.
Better compressed intermediate files.
Added utilities folder.
Added support for multiple short read files.
Removed use of configuration file.

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

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

BBSplit: Read Binning Tool for Metagenomes and Contaminated Libraries

Poonam Mahapatra — Wed, 03 Jan 2018 00:25:27 -0600

BBSplit internally uses BBMap to map reads to multiple genomes at once, and determine which genome they match best. This is different than with ordinary mapping. If a genome (say, human) contains an exact repeat somewhere, reads mapping to it will be mapped ambiguously. But if you want to determine whether reads are mouse or human, it does not matter whether they map ambiguously within human, only whether they are ambiguous between human and mouse. BBSplit tracks this additional ambiguity information and decides how to use it based on the “ambig2” flag. The normal use of BBSplit is like Seal, either quantifying how many reads go to each reference, or splitting the reads into multiple output files, one per reference. BBSplit can only be run using references indexed with BBSplit, as they contain additional information regarding which sequences came from which reference file.

BBSplit is a tool that bins reads by mapping to multiple references simultaneously, using BBMap. The reads go to the bin of the reference they map to best. There are also disambiguation options, such that reads that map to multiple references can be binned with all of them, none of them, one of them, or put in a special "ambiguous" file for each of them. Paired reads will always be kept together.

For example, if you had a library of something that was contaminated with e.coli and salmonella, you could do this:

bbsplit.sh in=reads.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu=clean.fq int=t

This will produce 3 output files:
out_ecoli.fq (ecoli reads)
out_salmonella.fq (salmonella reads)
clean.fq (unmapped reads)

In this case, "int=t" means that the input file is paired and interleaved. For single-end reads you would leave that out. For paired reads in 2 files, you would do this:
bbsplit.sh in1=reads1.fq in2=reads2.fq ref=ecoli.fa,salmonella.fa basename=out_%.fq outu1=clean1.fq outu2=clean2.fq

BBSplit is available here:
https://sourceforge.net/projects/bbmap/

The sensitivity can be raised to be equivalent to BBMap with these flags: "minratio=0.56 minhits=1 maxindel=16000"

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

rHAT: a seed-and-extension-based noisy long read alignment tool

Abhimanyu Singh — Sun, 23 Sep 2018 05:12:22 -0500

rHAT is a seed-and-extension-based noisy long read alignment tool. It is suitable for aligning 3rd generation sequencing reads which are in large read length with relatively high error rate, especially Pacbio's Single Molecule Read-time (SMRT) sequencing reads.

Address of the bookmark: https://github.com/dfguan/rHAT

URMAP, an ultra-fast read mapper

Jit — Thu, 29 Oct 2020 23:03:54 -0500

URMAP, a new read mapping algorithm. URMAP is an order of magnitude faster than BWA with comparable accuracy on several validation tests. On a Genome in a Bottle (GIAB) variant calling test with 30× coverage 2×150 reads, URMAP achieves high accuracy (precision 0.998, sensitivity 0.982 and F-measure 0.990) with the strelka2 caller. However, GIAB reference variants are shown to be biased against repetitive regions which are difficult to map and may therefore pose an unrealistically easy challenge to read mappers and variant callers.

More at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320720/

Address of the bookmark: https://github.com/rcedgar/urmap

BIGMAC : breaking inaccurate genomes and merging assembled contigs for long read metagenomic assembly

Jit — Mon, 22 May 2017 05:43:51 -0500

This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling. If you encounter any issues, please contact me at kklam@eecs.berkeley.edu. My name is Ka-Kit Lam.

https://github.com/kakitone/MetaFinisherSC

https://github.com/kakitone/BIGMAC

Address of the bookmark: https://github.com/kakitone/BIGMAC

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