BOL: Related items

splitbam: splits a BAM by chromosomes

Jit — Tue, 28 Feb 2017 09:01:28 -0600

splitbam splits a BAM by chromosomes.

Using the reference sequence dictionary (*.dict), it also creates some empty BAM files if no sam record was found for a chromosome. A pair of 'mock' SAM-Records can also be added to those empty BAMs to avoid some tools (like samtools) to crash.

Usage

java -jar splitbam.jar -p OUT/__CHROM__/__CHROM__.bam -R ref.fasta (bam|sam|stdin)

Options

-h help; This screen.
-R (indexed reference file) REQUIRED.
-u (unmapped chromosome name): default:Unmapped
-e | --empty : generate EMPTY bams for chromosome having no read mapped
-m | --mock : if option '-e', add a mock pair of sam records to the empty bam
-p (output file/bam pattern) REQUIRED. MUST contain __CHROM__ and end with .bam
-s assume input is sorted.
-x | --index create index.
-t | --tmp (dir) tmp file directory
-G (file) chrom-group file (see below)

Address of the bookmark: https://code.google.com/archive/p/jvarkit/wikis/SplitBam.wiki

Pacbio Long Reads Compatible Software and Tools

Archana Malhotra — Wed, 15 Mar 2017 14:19:01 -0500

The following software packages are known to be compatible with PacBio® data, in addition to PacBio's own SMRT® Analysis suite. All packages are believed to be open source or freely available for non-commercial use. See the individual project sites for up-to-date license information. A separate page lists commercial software.

Know of any other open source software for PacBio data? Email us.

Software categories:

Address of the bookmark: https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software

Meraculous: De Novo Genome Assembly with Short Paired-End Reads

Jit — Tue, 07 Nov 2017 04:36:10 -0600

We describe a new algorithm, meraculous, for whole genome assembly of deep paired-end short reads, and apply it to the assembly of a dataset of paired 75-bp Illumina reads derived from the 15.4 megabase genome of the haploid yeast Pichia stipitis. More than 95% of the genome is recovered, with no errors; half the assembled sequence is in contigs longer than 101 kilobases and in scaffolds longer than 269 kilobases. Incorporating fosmid ends recovers entire chromosomes. Meraculous relies on an efficient and conservative traversal of the subgraph of the k-mer (deBruijn) graph of oligonucleotides with unique high quality extensions in the dataset, avoiding an explicit error correction step as used in other short-read assemblers. A novel memory-efficient hashing scheme is introduced. The resulting contigs are ordered and oriented using paired reads separated by ∼280 bp or ∼3.2 kbp, and many gaps between contigs can be closed using paired-end placements. Practical issues with the dataset are described, and prospects for assembling larger genomes are discussed.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158087/

CoLoRMap: Correcting Long Reads by Mapping short reads

Jit — Mon, 20 Aug 2018 14:17:05 -0500

Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads.We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods.The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap

ehaghshe@sfu.ca or cedric.chauve@sfu.ca

Address of the bookmark: https://github.com/sfu-compbio/colormap

MGRA: Breakpoint graphs and ancestral genome reconstructions

Jit — Tue, 25 Jul 2017 08:48:25 -0500

MGRA (Multiple Genome Rearrangements and Ancestors) is a tool for reconstruction of ancestor genomes and evolutionary history of extant genomes.

It takes as an input a set of genomes represented as sequences of genes (or synteny blocks) and produces such sequences for ancestral genomes at the internal nodes of the phylogenetic tree.

The phylogenetic tree may be also specified completely or partially, in the latter case MGRA can reconstruct conserved ancestral regions (CARs) of the ancestral genome of interest.

Since version 2 MGRA supports gene insertion and deletions in addition to genome rearrangements and allows the input genomes to have different gene content.

It also can reconstruct most plausible phylogenetic tree based on the rearrangement characters.

Address of the bookmark: http://mgra.cblab.org/

Genomicus: genome browser that enables users to navigate in genomes in several dimensions

Jit — Sat, 18 Nov 2017 16:10:16 -0600

Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Address of the bookmark: http://genomicus.biologie.ens.fr/genomicus-90.01/cgi-bin/search.pl

Scripts for the analysis of HGT in genome sequence data.

Jit — Wed, 29 Nov 2017 16:44:10 -0600

Scripts for the analysis of HGT in genome sequence data

Address of the bookmark: https://github.com/reubwn/hgt

kSNP3.0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference genome

Jit — Fri, 08 Dec 2017 16:48:40 -0600

Sept. 20, 2017 Version 3.1 released. Major upgrade. Version 3.1 fixes the problems with SNP annotation that arose when NCBI discontinued use of GI numbers. Please read carefully the Preface (page 3) and the File of annotated genomes section (pages 9-10) in the version 3.1 User Guide. Thanks to Tom Slezak for revsing the get_genbank_file3 script and to Tod Stuber (USDA) for testing version 3.1 even though he doesn't need the annotation feature. All users are encouraged to upgrade to version 3.1.

Address of the bookmark: https://sourceforge.net/projects/ksnp/files/

String graph based genome assembly software and tools !

Rahul Nayak — Tue, 19 Dec 2017 17:17:38 -0600

In graph theory, a string graph is an intersection graph of curves in the plane; each curve is called a "string". String graphs were first proposed by E. W. Myers in a 2005 publication. In recent Genome Research paper describing an innovative approach for assembling large genomes from NGS data caught our attention for several reasons. i) it give different "string graph" prospective of long lasting genome assembly problem ii) the paper is coauthored by Jared Simpson, the developer of ABySS assembler and Richard Durbin. iii) Simpson-Durbin algorithm is that it does not rely on de Bruijn graphs, and instead employs a different graph construction approach called ‘string graph’.

Following are the genome assembly tools based on string graph:

1.SGA (String Graph Assembler) https://github.com/jts/sga

Assembles large genomes from high coverage short read data. SGA is designed as a modular set of programs, which are used to form an assembly pipeline. SGA implements a set of assembly algorithms based on the FM-index. As the FM-index is a compressed data structure, the algorithms are very memory efficient. The SGA assembly has three distinct phases. The first phase corrects base calling errors in the reads. The second phase assembles contigs from the corrected reads. The third phase uses paired end and/or mate pair data to build scaffolds from the contigs. The output of this software is a PDF report that allows the properties of the genome and data quality to be visually explored. By providing more information to the user at the start of an assembly project, this software will help increase awareness of the factors that make a given assembly easy or difficult, assist in the selection of software and parameters and help to troubleshoot an assembly if it runs into problems.

2. SAGE: String-overlap Assembly of GEnomes https://github.com/lucian-ilie/SAGE2

SAGE, for de novo genome assembly. As opposed to most assemblers, which are de Bruijn graph based, SAGE uses the string-overlap graph. SAGE builds upon great existing work on string-overlap graph and maximum likelihood assembly, bringing an important number of new ideas, such as the efficient computation of the transitive reduction of the string overlap graph, the use of (generalized) edge multiplicity statistics for more accurate estimation of read copy counts, and the improved use of mate pairs and min-cost flow for supporting edge merging. The assemblies produced by SAGE for several short and medium-size genomes compared favourably with those of existing leading assemblers.

3. FSG: Fast String Graph

The new integrated assembler has been assessed on a standard benchmark, showing that fast string graph (FSG) is significantly faster than SGA while maintaining a moderate use of main memory, and showing practical advantages in running FSG on multiple threads. Moreover, we have studied the effect of coverage rates on the running times.

4. BASE https://github.com/dhlbh/BASE

It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs. BASE is a practically efficient tool for constructing contig, with significant improvement in quality for long NGS reads. It is relatively easy to extend BASE to include scaffolding.

5. Fermi https://github.com/lh3/fermi/

Fermi is a de novo assembler with a particular focus on assembling Illumina short sequence reads from a mammal-sized genome. In addition to the role of a typical assembler, fermi also aims to preserve heterozygotes which are often collapsed by other assemblers. Its ultimate goal is to find a minimal set of unitigs to represent all the information in raw reads.

If you want to learn about String Graph assembler, please read the following papers -

i) The Fragment Assembly String Graph - E. W. Myers

This paper describes the String Graph concept.

ii) Efficient construction of an assembly string graph using the FM-index - Jared T. Simpson and Richard Durbin

This earlier paper from Simpson and Durbin

iii) Efficient de novo assembly of large genomes using compressed data structures - Jared T. Simpson and Richard Durbin

AliTV—interactive visualization of whole genome comparisons

Jit — Wed, 10 Jan 2018 07:08:17 -0600

AliTV, which provides interactive visualization of whole genome alignments. AliTV reads multiple whole genome alignments or automatically generates alignments from the provided data. Optional feature annotations and phylo- genetic information are supported. The user-friendly, web-browser based and highly customizable interface allows rapid exploration and manipulation of the visualized data as well as the export of publication-ready high-quality figures. AliTV is freely available at https://github.com/AliTVTeam/AliTV

https://alitvteam.github.io/AliTV/

Address of the bookmark: https://github.com/AliTVTeam/AliTV