BOL: Related items

MyCC: Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes

Jit — Fri, 03 Mar 2017 08:34:23 -0600

MyCC, an automated binning tool that combines genomic signatures, marker genes and optional contig coverages within one or multiple samples, in order to visualize the metagenomes and to identify the reconstructed genomic fragments.

More at http://www.nature.com/articles/srep24175

Address of the bookmark: https://sourceforge.net/projects/sb2nhri/files/MyCC/

MaxBin: software for binning assembled metagenomic sequences based on an Expectation-Maximization algorithm.

Jit — Mon, 06 Mar 2017 04:03:38 -0600

MaxBin is software for binning assembled metagenomic sequences based on an Expectation-Maximization algorithm. Users can understand the underlying bins (genomes) of the microbes in their metagenomes by simply providing assembled metagenomic sequences and the reads coverage information or sequencing reads. For users' convenience MaxBin will report genome-related statistics, including estimated completeness, GC content and genome size in the binning summary page.

Users can use MEGAN or similar software on MaxBin bins to find the taxonomy of each bin after the binning process is finished.

https://academic.oup.com/bioinformatics/article/32/4/605/1744462/MaxBin-2-0-an-automated-binning-algorithm-to

The most recent version of MaxBin is 2.2, which supports the analysis of coassemblies of multiple samples. It is available at this JBEI downloads sites as well as MaxBin and MaxBin 2.0 sourceforge sites.

Address of the bookmark: http://downloads.jbei.org/data/microbial_communities/MaxBin/MaxBin.html

GroopM: Metagenomic binning toolset

Jit — Tue, 07 Mar 2017 08:59:45 -0600

GroopM is a metagenomic binning toolset. It leverages spatio-temoral
dynamics (differential coverage) to accurately (and almost automatically)
extract population genomes from multi-sample metagenomic datasets.

GroopM is largely parameter-free. Use: groopm -h for more info.

For installation and usage instructions see : http://ecogenomics.github.io/GroopM/

Address of the bookmark: https://github.com/ecogenomics/GroopM

Software and Tools to detect structure variation with long reads !!

Archana Malhotra — Wed, 15 Mar 2017 14:31:09 -0500

Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio de novo assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.

With Single Molecule, Real-Time (SMRT) Sequencing, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:

Uncover missing heritability linked to structural variation
Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease
Resolve structural variation across the complete size spectrum with basepair resolution

Following are the SV tools, which can assist you to achieve your goal.

Sniffles: Structural variation caller using third generation sequencing

Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!

More at https://github.com/fritzsedlazeck/Sniffles

MultiBreak-SV: It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.

There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.

More at https://github.com/raphael-group/multibreak-sv

Parliament: A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?

Publication about the algorithm and “…the first long-read characterization of structural variation in a diploid human personal genome…” (HS1011) - “Assessing structural variation in a personal genome—towards a human reference diploid genome”

More at https://sourceforge.net/projects/parliamentsv/

https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf

PBHoney: the structural variation discovery tool

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Read The Paper http://www.biomedcentral.com/1471-2105/15/180/abstract

More at https://sourceforge.net/projects/pb-jelly/

SMRT-SV: Structural variant and indel caller for PacBio reads

Structural variant (SV) and indel caller for PacBio reads based on methods from Chaisson et al. 2014.

SMRT-SV provides an official software package for tools described in Chaisson et al. 2014 and adds several key features including the following.

Unified variant calling user interface with built-in cluster compute support
Small indel calling (2-49 bp)
Improved inversion calling (screenInversions)
Quality metric for SV calls based on number of local assemblies supporting each call
Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions
Genotyping of SVs with Illumina paired-end reads from WGS samples

More at https://github.com/EichlerLab/pacbio_variant_caller

Bacterial genome assembly !!

Jit — Fri, 05 May 2017 06:11:22 -0500

This tutorial will serve as an example of how to use free and open-source genome assembly and secondary scaffolding tools to generate high quality assemblies of bacterial sequence data. The bacterial sample used in this tutorial will be referred to simply as “Species” since it is live data. This data is paired-end data, meaning that there are forward and reverse reads, which we will designate as Sample_R1.fastq and Sample_R2.fastq, respectively.

https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md

Address of the bookmark: http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/

NCBI Prokaryotic Genome Annotation Pipeline

Jit — Tue, 16 May 2017 08:56:03 -0500

NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. You can find a more detailed description of the new version of the pipeline in NCBI Handbook chapter. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

DESCHRAMBLER

Jit — Thu, 29 Jun 2017 11:54:59 -0500

DESCHRAMBLER is shown to produce highly accurate reconstructions using data simulation and by benchmarking it against other reconstruction tools

You can find the detail of reconstructed data at http://bioinfo.konkuk.ac.kr/DESCHRAMBLER/

Address of the bookmark: https://github.com/jkimlab/DESCHRAMBLER

Rectangle Graph for Repeat Resolution in Genome Assembly

Rahul Nayak — Thu, 28 Dec 2017 09:43:03 -0600

Ultimate tool for resolving repeats in genome assemblies.

Though the specific implementation of the idea of the rectangle graph approach is already included into the current SPAdes distribution, we're also releasing the Rectangle Graph Module (RGM) as the separate code which can be run independently of SPAdes. Although RGM differs from the current implementation of the rectangle graph approach in SPAdes, in the future we plan to integrate RGM in SPAdes. RGM can be run with other genome assemblers if they use the graph format as SPAdes files.

For more details see: Nikolay Vyahhi, Son K. Pham, Pavel Pevzner. From de Bruijn Graphs to Rectangle Graphs for Genome Assembly, Lecture Notes in Bioinformatics 7534 (2012), pp. 249-261.

Address of the bookmark: http://bioinf.spbau.ru/en/rectangles

Genome assembly stats plotting

Jit — Wed, 28 Feb 2018 03:45:39 -0600

A de novo genome assembly can be summarised b

y a number of metrics, including:

Overall assembly length
Number of scaffolds/contigs
Length of longest scaffold/contig
Scaffold/contig N50 and N90Assembly base composition, in particular percentage GC and percentage Ns
CEGMA completeness
Scaffold/contig length/count distribution

assembly-stats supports two widely used presentations of these values, tabular and cumulative length plots, and introduces an additional circular plot that summarises most commonly used assembly metrics in a single visualisation. Each of these presentations is generated using javascript from a common (JSON) data structure, allowing toggling between alternative views, and each can be applied to a single or multiple assemblies to allow direct comparison of alternate assemblies.

Tabular presentation allows direct comparison of exact values between assemblies, the limitations of this approach lie in the necessary omission of distributions and the challenge of interpreting ratios of values that may vary by several orders of magnitude.

Address of the bookmark: https://github.com/rjchallis/assembly-stats

Earth BioGenome Project

Jit — Wed, 25 Apr 2018 07:48:56 -0500

The central goal of the Earth BioGenome Project is to understand the evolution and organization of life on our planet by sequencing and functionally annotating the genomes of 1.5 million known species of eukaryotes, a massive group that includes plants, animals, fungi and other organisms whose cells have a nucleus that houses their chromosomal DNA. To date, the genomes of less than 0.2 percent of eukaryotic species have been sequenced.

More at https://www.ucdavis.edu/news/earth-biogenome-project-aims-sequence-dna-all-complex-life