BOL: Related items

DISCOVAR

Abhimanyu Singh — Mon, 18 Apr 2016 11:59:16 -0500

DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer — produced on MiSeq or HiSeq 2500 — and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.

DISCOVAR can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.

DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants.

More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14

Address of the bookmark: https://www.broadinstitute.org/software/discovar/blog/

Blobology

Jit — Mon, 13 Jun 2016 10:18:33 -0500

Tools for making blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step

Blaxter Lab, Institute of Evolutionary Biology, University of Edinburgh

Goal: To create blobplots or Taxon-Annotated-GC-Coverage plots (TAGC plots) to visualise the contents of genome assembly data sets as a QC step.

This repository accompanies the paper:
Blobology: exploring raw genome data for contaminants, symbionts and parasites using taxon-annotated GC-coverage plots. Sujai Kumar, Martin Jones, Georgios Koutsovoulos, Michael Clarke, Mark Blaxter
(submitted 2013-10-01 to Frontiers in Bioinformatics and Computational Biology special issue : Quality assessment and control of high-throughput sequencing data).

It contains bash/perl/R scripts for running the analysis presented in the paper to create a preliminary assembly, and to create and collate GC content, read coverage and taxon annotation for the preliminary assembly, which can be visualised, such as Figure 2a from the paper showing TAGC plots/blobplots for Caenorhabditis sp. 5:

Address of the bookmark: https://github.com/blaxterlab/blobology

HGA

Jit — Tue, 29 Nov 2016 07:25:53 -0600

HGA tool version 1.0 This tool helps to apply the Hierarchical Genome Assembly (HGA) method. The tool will apply: 1. Partitioning a given reads dataset into a given number of partitions. 2. Assembling each partitions using a pre-specified assembler (Velvet or SPAdes in this version) and using a given kmer size. 3. Merging all the assemblies of the partition. 4. Combining all the assemblies of the partition (using velvet with kmer value of 31). 5. Finaly, re-assembling the whole dataset with the merged contigs or the combined contigs, using a given kmer size.

https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA

Address of the bookmark: https://github.com/aalokaily/Hierarchical-Genome-Assembly-HGA

CISA: Contig Integrator for Sequence Assembly

Neel — Thu, 15 Dec 2016 05:42:21 -0600

A plethora of algorithmic assemblers have been proposed for the de novo assembly of genomes, however, no individual assembler guarantees the optimal assembly for diverse species. Optimizing various parameters in an assembler is often performed in order to generate the most optimal assembly. However, few efforts have been pursued to take advantage of multiple assemblies to yield an assembly of high accuracy. In this study, we employ various state-of-the-art assemblers to generate different sets of contigs for bacterial genomes. A tool, named CISA, has been developed to integrate the assemblies into a hybrid set of contigs, resulting in assemblies of superior contiguity and accuracy, compared with the assemblies generated by the state-of-the-art assemblers and the hybrid assemblies merged by existing tools. This tool is implemented in Python and requires MUMmer and BLAST+ to be installed on the local machine. The source code of CISA and examples of its use are available at http://sb.nhri.org.tw/CISA/.

Address of the bookmark: http://sb.nhri.org.tw/CISA/en/CISA

HivePlot

Jit — Thu, 16 Feb 2017 11:39:34 -0600

The hive plot is a rational visualization method for drawing networks. Nodes are mapped to and positioned on radially distributed linear axes — this mapping is based on network structural properties. Edges are drawn as curved links. Simple and interpretable.

The purpose of the hive plot is to establish a new baseline for visualization of large networks — a method that is both general and tunable and useful as a starting point in visually exploring network structure.

More at http://www.hiveplot.com/

Address of the bookmark: http://www.hiveplot.com/

ConPADE: Genome Assembly Ploidy Estimation from Next-Generation Sequencing Data

Jit — Fri, 24 Feb 2017 04:55:41 -0600

ConPADE (Contig Ploidy and Allele Dosage Estimation), a probabilistic method that estimates the ploidy of any given contig/scaffold based on its allele proportions. In the process, they report findings regarding errors in sequencing. The method can be used for whole genome shotgun (WGS) sequencing data. They also show applicability of the method for variant calling and allele dosage estimation. Results for simulated and real datasets are discussed and provide evidence that ConPADE performs well as long as enough sequencing coverage is available, or the true contig ploidy is low.

https://github.com/microsoftgenomics

Address of the bookmark: https://github.com/microsoftgenomics

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/

SeqMule: Automated human exome/genome variants detection

Abhimanyu Singh — Tue, 07 Mar 2017 10:12:36 -0600

SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file. SeqMule also has some built-in functions, such as pooling consensus calls from various callers, plotting a Venn diagram showing intersection among different callers, and downloading databases. SeqMule can be used for both Mendelian disease study and cancer genome study.

Address of the bookmark: http://seqmule.openbioinformatics.org/en/latest/

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/

Download assemblies from NCBI

Bulbul — Mon, 15 May 2017 06:02:32 -0500

A new “Download assemblies” button is now available in the Assembly database. This makes it easy to download data for multiple genomes without having to write scripts.

For example, you can run a search in Assembly and use check boxes (see left side of screenshot below) to refine the set of genome assemblies of interest. Then, just open the “Download assemblies” menu, choose the source database (GenBank or RefSeq), choose the file type, and start the download. An archive file will be saved to your computer that can be expanded into a folder containing your selected genome data files.

More at https://ncbiinsights.ncbi.nlm.nih.gov/2017/05/08/genome-data-download-made-easy/