BOL: Related items

JBrowse: Embeddable genome browser built completely with JavaScript and HTML5

Jit — Fri, 29 Jun 2018 09:19:56 -0500

JBrowse is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl. Headline Features: Fast, smooth scrolling and zooming. Explore your genome with unparalleled speed. Scales easily to multi-gigabase genomes and deep-coverage sequencing. Quickly open and view data files on your computer without uploading them to any server. Supports GFF3, BED, FASTA, Wiggle, BigWig, BAM, VCF (with either .tbi or .idx index), REST, and more. BAM, BigBed, BigWig, and VCF data are displayed directly from chunks of the compressed binary files, no conversion needed. Includes an optional “faceted” track selector (see demo) suitable for large installations with thousands of tracks. Very light server resource requirements. In fact, JBrowse has no back-end server code, just tools for formatting data files to be read directly over HTTP. Serve huge datasets from a single low-cost cloud instance. Can run as a stand-alone app on OSX and Windows using the Electron platform Highly extensible plugin architecture, with a large plugin registry of existing examples here https://gmod.github.io/jbrowse-registry https://jbrowse.org/

Address of the bookmark: https://github.com/GMOD/jbrowse

Hercules: a profile HMM-based hybrid error correction algorithm for long reads

Jit — Mon, 20 Aug 2018 14:14:11 -0500

Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time- efficient machine learning algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results: We designed and developed Hercules, the first machine learning-based long read error correction algorithm. The algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platformtextquoterights error profile. The algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability:

Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Address of the bookmark: https://github.com/BilkentCompGen/Hercules

Rainbow: an integrated tool for efficient clustering and assembling RAD-seq reads

Rahul Nayak — Fri, 19 Oct 2018 08:23:42 -0500

Rainbow is developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq. First, Rainbow clusters reads using a spaced seed method. Then, Rainbow implements a heterozygote calling like strategy to divide potential groups into haplotypes in a top–down manner. And along a guided tree, it iteratively merges sibling leaves in a bottom–up manner if they are similar enough. Here, the similarity is defined by comparing the 2nd reads of a RAD segment. This approach tries to collapse heterozygote while discriminate repetitive sequences. At last, Rainbow uses a greedy algorithm to locally assemble merged reads into contigs. Rainbow not only outputs the optimal but also suboptimal assembly results. Based on simulation and a real guppy RAD-seq data, we show that Rainbow is more competent than the other tools in dealing with RAD-seq data

Address of the bookmark: https://sourceforge.net/projects/bio-rainbow/files/

Genome assembly tutorial "Genome Assembly for short and long reads"

Jit — Sat, 19 Jan 2019 17:29:53 -0600

In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:

How to perform basic quality checks on the input data
How to run a short read assembler on Illumina data
How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data
How to improve the accuracy of a long read assembly using short reads
How to assess the quality of an assembly

https://bioinformaticsdotca.github.io/high-throughput_biology_2017

Address of the bookmark: https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab

Biological databases !

BioStar — Wed, 12 Feb 2020 01:16:29 -0600

Now a days there are a lots of genomics databases available around the world. This bookmark is created to provide all links in one place ...

ftp://ftp.ncbi.nih.gov/genomes/

https://hgdownload.soe.ucsc.edu/downloads.html

Address of the bookmark: ftp://ftp.ncbi.nih.gov/genomes/

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

BioStar — Mon, 17 Aug 2020 05:21:09 -0500

SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The bioinformatics treatment is based on Vsearch [Rognes 2016] which showed to be both accurate and fast [Wescott 2015].The statistical analysis is based on DESeq2 R package [Anders and Huber 2010] which robustly identifies the differential abundant features as suggested in [McMurdie and Holmes 2014] and [Jonsson2016]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [Love 2014].
SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available here and a poster here.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4

Address of the bookmark: https://github.com/aghozlane/shaman

HairSplitter: assembling long reads in an unknown number of haplotypes

BioStar — Wed, 07 Dec 2022 00:13:40 -0600

Pros and cons of HairSplitter Limitations of HairSplitter:

Not very fast: it re-polishes the whole assembly

Limited in the number of haplotypes

Strengths of HairSplitter:

Very modular, can be used with any assembler

Naive: makes no assumption on ploidy, parameter-free

Safe: won’t artificially duplicate contigs

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

HairSplitter splits collapsed assemblies from “draft” assemblies obtained by any means

HairSplitter can recover haplotypes and distinguish repeated elements

Only needs sequencing reads, potentially error-prone

Not really available yet (github.com/RolandFaure/HairSplitter)

https://hal.archives-ouvertes.fr/hal-03864075/file/RolandFaure_presentation_SeqBIM_2022.pdf

Address of the bookmark: https://hal.archives-ouvertes.fr/hal-03817928/document

Stacks

Jitendra Narayan — Wed, 24 Feb 2016 15:52:30 -0600

Stacks is a software pipeline for building loci from short-read sequences, such as those generated on the Illumina platform. Stacks was developed to work with restriction enzyme-based data, such as RAD-seq, for the purpose of building genetic maps and conducting population genomics and phylogeography.

More at http://catchenlab.life.illinois.edu/stacks/

Address of the bookmark: http://catchenlab.life.illinois.edu/stacks/

mrFAST: Micro Read Fast Alignment Search Tool

Neel — Tue, 26 Apr 2016 03:50:06 -0500

mrFAST is a read mapper that is designed to map short reads to reference genome with a special emphasis on the discovery of structural variation and segmental duplications. mrFAST maps short reads with respect to user defined error threshold, including indels up to 4+4 bp. This manual, describes how to choose the parameters and tune mrFAST with respect to the library settings. mrFAST is designed to find 'all' mappings for a given set of reads, however it can return one "best" map location if the relevant parameter is invoked.

More at http://mrfast.sourceforge.net/manual.html

Address of the bookmark: http://mrfast.sourceforge.net/manual.html

ART: Set of Simulation Tools

Jit — Thu, 03 Nov 2016 08:28:25 -0500

ART is a set of simulation tools to generate synthetic next-generation sequencing reads. ART simulates sequencing reads by mimicking real sequencing process with empirical error models or quality profiles summarized from large recalibrated sequencing data. ART can also simulate reads using user own read error model or quality profiles. ART supports simulation of single-end, paired-end/mate-pair reads of three major commercial next-generation sequencing platforms: Illumina's Solexa, Roche's 454 and Applied Biosystems' SOLiD. ART can be used to test or benchmark a variety of method or tools for next-generation sequencing data analysis, including read alignment, de novo assembly, SNP and structure variation discovery. ART was used as a primary tool for the simulation study of the 1000 Genomes Project . ART is implemented in C++ with optimized algorithms and is highly efficient in read simulation. ART outputs reads in the FASTQ format, and alignments in the ALN format. ART can also generate alignments in the SAM alignment or UCSC BED file format. ART can be used together with genome variants simulators (e.g. VarSim) for evaluating variant calling tools or methods.

Address of the bookmark: http://www.niehs.nih.gov/research/resources/software/biostatistics/art/