BOL: Related items

E-MEM: Efficient computation of Maximal Exact Matches

Jit — Thu, 15 Dec 2016 09:30:43 -0600

E-MEM is a C++/OpenMP program designed to efficiently compute MEMs between large genomes. See the README file for instructions on how to use E-MEM.

E-MEM source code

The source code can be downloaded here.

If you use E-MEM, please cite:

N. Khiste, L. Ilie, E-MEM: Efficient computation of Maximal Exact Matches for very large genomes, Bioinformatics 31(4) (2015) 509 -- 514.

For any questions, please contact Lucian Ilie: ilie@uwo.ca

Address of the bookmark: http://www.csd.uwo.ca/~ilie/E-MEM/

QUAST: quality assessment tool for genome assemblies

Jitendra Prajapati — Wed, 06 Apr 2016 18:23:33 -0500

QUAST evaluates genome assemblies. For metagenomes, please see MetaQUAST project.
It can works both with and without a given reference genome.
The tool accepts multiple assemblies, thus is suitable for comparison.

More at http://bioinf.spbau.ru/quast

http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

YAHA

Jit — Fri, 20 Jan 2017 05:38:05 -0600

YAHA, a fast and flexible hash-based aligner. YAHA is as fast and accurate as BWA-SW at finding the single best alignment per query and is dramatically faster and more sensitive than both SSAHA2 and MegaBLAST at finding all possible alignments. Unlike other aligners that report all, or one, alignment per query, or that use simple heuristics to select alignments, YAHA uses a directed acyclic graph to find the optimal set of alignments that cover a query using a biologically relevant breakpoint penalty. YAHA can also report multiple mappings per defined segment of the query. We show that YAHA detects more breakpoints in less time than BWA-SW across all SV classes, and especially excels at complex SVs comprising multiple breakpoints.

Availability: YAHA is currently supported on 64-bit Linux systems. Binaries and sample data are freely available for download from http://faculty.virginia.edu/irahall/YAHA.

Contact:

http://genome.wustl.edu/people/groups/detail/hall-lab/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3463118/

Mercator

Jit — Mon, 06 Feb 2017 04:20:36 -0600

Our basic strategy in building homology maps is to use exons that are orthologous in multiple genomes as map "anchors." Given K genomes, the steps in the map construction are as follows:

For each genome, obtain a set of exon annotations. These annotations can be a combination of both exon predictions (e.g. Genscan) and annotations that have been experimentally verified (e.g. RefSeq). Ideally, we would like to have these annotations be as sensitive as possible. Specificity is not a concern, as incorrect annotations are not likely not have significant alignments with other gene annotations.
Compare all exons against all exons in other genomes and record significant alignments between exons. Currently, we use BLAT to do this all-vs-all comparison with alignments being performed in protein space.
Construct a graph with each vertex corresponding to a exon and edges between vertices whose corresponding exons have significant alignments.
Identify cliques in this graph. These cliques are potential anchors to be used in the map.
Starting with the largest cliques (those that have exons in all or most of the genomes), join neighboring (adjacent in genomic coordinates, in each genome) cliques to form runs. Smaller cliques that are inconsistent with runs formed by larger cliques are filtered out. After the smallest cliques have been considered, cliques that are not part of a run are discarded.
The extents of each run in each genome are outputted as orthologous segments. The cliques from each run are used to output the exact genomic coordinates of anchors within each orthologous segment. These anchors can be used by genomic alignment programs (such as MAVID) to do a detailed alignment of each orthologous segment.

https://www.biostat.wisc.edu/~cdewey/mercator/

Address of the bookmark: https://www.biostat.wisc.edu/~cdewey/mercator/

DIAL

Abhimanyu Singh — Wed, 01 Mar 2017 08:42:28 -0600

A computational pipeline for identifying single-base substitutions between two closely related genomes without the help of a reference genome. DIAL works even when the depth of coverage is insufficient for de novo assembly, and it can be extended to determine small insertions/deletions. Our main motivation is to use this tool to survey the genetic diversity of endangered species as the identified sequence differences can be used to design genotyping arrays to assist in the species' management.

http://www.bx.psu.edu/~ratan/

Address of the bookmark: http://www.bx.psu.edu/miller_lab/

CLgenomics

Radha Agarkar — Fri, 03 Mar 2017 09:57:28 -0600

CLgenomics is a standalone desktop software specifically designed for bacterial genome analysis. This program has a powerful multi-genome browser, which enables rapid and responsive exploration of bacterial genomes.

To use CLgenomics, individual genome data (genome sequences + annotation details) are compiled and saved in a specially formatted file called CLG (ChunLab Genomics). Each CLG file corresponds with one bacterial genome. If multiple genomes are being considered and compared, multiple CLG files are needed. ChunLab offers >40,000 CLG files of publicly available Bacterial and Archaeal genomes.

Address of the bookmark: https://chunlab.wordpress.com/clgenomics-software/

LoRDEC: a hybrid error correction program for long, PacBio reads

Jit — Mon, 10 Apr 2017 04:16:09 -0500

LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.

Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.
After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.

Why is LoRDEC different?

It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

Address of the bookmark: http://www.atgc-montpellier.fr/lordec/

DeCoSTAR - Detection of Co-evolution

Jit — Fri, 14 Apr 2017 06:27:25 -0500

DeCoSTAR is a software which aims at reconstructing ancestral gene or genome organizations, in the form of sets of neighborhood relations -adjacencies- between pairs of ancestral genes or gene domains.
Ancestral genes or domains are deduced from reconciled gene trees in a context of birth, speciation, duplication, loss, transfer, which are either given as input or computed with the ecceTERA package, to which DeCoSTAR is integrated. DeCoSTAR constructs parsimonious scenarios of gains and breakages of adjacencies, and contains in particular all the features of previous software DeCo, DeCoLT, ArtDeCo and DeClone. It provides statistical supports on ancestral adjacencies, or the possibility to handle badly assembled genomes.
DeCoSTAR is able to reconstruct the histories of domains inside genes, including gene fusion and fission events, as well as ancestral genome structures for dozens of whole genomes from all kingdoms of life in a few minutes.

Address of the bookmark: http://pbil.univ-lyon1.fr/software/DeCoSTAR/

A Post-assembly genome-improvement toolkit (PAGIT) to obtain annotated genomes from contigs

Abhimanyu Singh — Fri, 12 May 2017 10:50:29 -0500

PAGIT addresses the need for software to generate high quality draft genomes. It is based on a series of programs that we developed:

ABACAS, that is able to contiguate contigs from a de novo assembly against a closely related reference.

IMAGE, an iterative approach for closing gaps in assembled genomes using mate pair information. It is able to close gaps left open by the assembler in a draft genome, even when using the same data sets as used by the original assembler.

iCORN, that enables errors in the consensus sequence to be corrected by iteratively mapping reads to the current assembly. An improved version, especially correction Pacfic Bioscience assemblies (PacBio) can be found here.

RATT, a tool to transfer the annotation from a reference genome, or an earlier assembly, onto the latest assembly.

PAGIT bundles these software and makes them more accessible for users.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/pagit

Fastq format

Jit — Wed, 03 May 2017 04:23:32 -0500

FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores. Both the sequence letter and quality score are each encoded with a single ASCII character for brevity.

It was originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data, but has recently become the de facto standard for storing the output of high-throughput sequencing instruments such as the Illumina Genome Analyzer.^[1]

Address of the bookmark: https://en.wikipedia.org/wiki/FASTQ_format