BOL: Related items

A web-based tool for sequence alignment statistics and innovative visualization

BioStar — Thu, 04 Apr 2024 01:44:50 -0500

AlignStatPlot, a new R package and online tool that is well-documented and easy-to usefor MSA and post-MSA analysis. This tool performs both traditional and cutting-edge analy-ses on sequencing data and generates new visualisation methods for MSA results. Whencompared to currently available tools, AlignStatPlot provides a robust ability to handle andvisualise diversity data, while the online version will save time and encourage researchersto focus on explaining their findings. It is a simple tool that can be used in conjunction withpopulation genetics software (PDF) AlignStatPlot: An R package and online tool for robust sequence alignment statistics and innovative visualization of big data.

Address of the bookmark: https://bioinformatics.um6p.ma/AlignStatPlot/

WhatsHap: fast and accurate read-based phasing

Jit — Mon, 28 May 2018 09:52:16 -0500

WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads.

Features

Very accurate results (Martin et al., WhatsHap: fast and accurate read-based phasing)

Works well with Illumina, PacBio, Oxford Nanopore and other types of reads

It phases SNVs, indels and even “complex” variants (such as TCG → AGAA)

Pedigree phasing mode uses reads from related individuals (such as trios) to improve results and to reduce coverage requirements (Garg et al., Read-Based Phasing of Related Individuals).

WhatsHap is easy to install

It is easy to use: Pass in a VCF and one or more BAM files, get out a phased VCF. Supports multi-sample VCFs.

It produces standard-compliant VCF output by default

If desired, get output that is compatible with ReadBackedPhasing

Open Source (MIT license)

Address of the bookmark: https://whatshap.readthedocs.io/en/latest/

Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads

BioStar — Tue, 04 Feb 2020 23:23:16 -0600

Rcorrector has an accuracy higher than or comparable to existing methods, including the only other method (SEECER) designed for RNA-seq reads, and is more time and memory efficient. With a 5 GB memory footprint for 100 million reads, it can be run on virtually any desktop or server. The software is available free of charge under the GNU General Public License from https://github.com/mourisl/Rcorrector/.

Usage: perl run_rcorrector.pl [OPTIONS]
OPTIONS:
	Required
	-s seq_files: comma separated files for single-end data sets
	-1 seq_files_left: comma separated files for the first mate in the paried-end data sets
	-2 seq_files_right: comma separated files for the second mate in the paired-end data sets
	-i seq_files_interleaved: comma sperated files for interleaved paired-end data sets
	Optional
	-k INT: kmer_length (<=32, default: 23)
	-od STRING: output_file_directory (default: ./)
	-t INT: number of threads to use (default: 1)
	-trim : allow trimming (default: false)
	-maxcorK INT: the maximum number of correction within k-bp window (default: 4)
	-wk FLOAT: the proportion of kmers that are used to estimate weak kmer count threshold, lower for more divergent genome (default: 0.95)
	-ek INT: expected number of kmers; does not affect the correctness of program but affects the memory usage (default: 100000000)
	-stdout: output the corrected reads to stdout (default: not used)
	-verbose: output some correction information to stdout (default: not used)
	-stage INT: start from which stage (default: 0)
		0-start from begining(storing kmers in bloom filter) ;
		1-start from count kmers showed up in bloom filter;
		2-start from dumping kmer counts into a jf_dump file;
		3-start from error correction.

Address of the bookmark: https://github.com/mourisl/Rcorrector/

PuffAligner: a fast, efficient and accurate aligner based on the Pufferfish index

Rahul Nayak — Thu, 21 Apr 2022 05:41:39 -0500

PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space and accuracy tradeoffs made by different alignment tools and provides a promising foundation on which to test new alignment ideas over large collections of sequences.

Address of the bookmark: https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

Neel — Sun, 20 Dec 2020 01:39:54 -0600

The first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. SneakySnake greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short (Illumina) and long (ONT and PacBio) reads. Described by Alser et al. (preliminary version at https://arxiv.org/abs/1910.09020).

Address of the bookmark: https://github.com/CMU-SAFARI/SneakySnake

Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data

Rahul Nayak — Mon, 13 Nov 2017 05:10:23 -0600

Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats

Address of the bookmark: https://github.com/OpenGene/AfterQC

MinION_GC: An R script to do some QC on MinION data

Radha Agarkar — Sun, 03 Dec 2017 15:19:18 -0600

Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki

kallisto: a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data

Jit — Mon, 07 Jan 2019 10:35:14 -0600

kallisto is a program for quantifying abundances of transcripts from bulk and single-cell RNA-Seq data, or more generally of target sequences using high-throughput sequencing reads. It is based on the novel idea of pseudoalignment for rapidly determining the compatibility of reads with targets, without the need for alignment. On benchmarks with standard RNA-Seq data, kallisto can quantify 30 million human reads in less than 3 minutes on a Mac desktop computer using only the read sequences and a transcriptome index that itself takes less than 10 minutes to build. Pseudoalignment of reads preserves the key information needed for quantification, and kallisto is therefore not only fast, but also as accurate as existing quantification tools. In fact, because the pseudoalignment procedure is robust to errors in the reads, in many benchmarks kallisto significantly outperforms existing tools. kallisto is described in detail in:

Nicolas L Bray, Harold Pimentel, Páll Melsted and Lior Pachter, Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology 34, 525–527 (2016), doi:10.1038/nbt.3519

Address of the bookmark: https://pachterlab.github.io/kallisto/about

heatmaply: popular graphical method for visualizing high-dimensional data

Neel — Sat, 11 Jan 2020 07:34:14 -0600

This work is based on ggplot2 and plotly.js engine. It produces similar heatmaps as d3heatmap, with the advantage of speed (plotly.js is able to handle larger size matrix), and the ability to zoom from the dendrogram.

heatmaply also provides an interface based around the plotly R package. This interface can be used by choosing plot_method = "plotly" instead of the default plot_method = "ggplot". This interface can provide smaller objects and faster rendering to disk in many cases and provides otherwise almost identical features.

Documentation for this package is also available as a pkgdown site: http://talgalili.github.io/heatmaply/

Address of the bookmark: http://talgalili.github.io/heatmaply/articles/heatmaply.html