BOL: Related items

pyScaf

Bulbul — Mon, 19 Dec 2016 14:20:33 -0600

pyScaf orders contigs from genome assemblies utilising several types of information:

paired-end (PE) and/or mate-pair libraries (NGS-based mode)
long reads (NGS-based mode)
synteny to the genome of some related species (reference-based mode)

Scaffolding

In reference-based mode, pyScaf uses synteny to the genome of closely related species in order to order contigs and estimate distances between adjacent contigs.

Contigs are aligned globally (end-to-end) onto reference chromosomes, ignoring:

matches not satisfying cut-offs (--identity and --overlap)
suboptimal matches (only best match of each query to reference is kept)
and removing overlapping matches on reference.

In preliminary tests, pyScaf performed superbly on simulated heterozygous genomes based on C. parapsilosis (13 Mb; CANPA) and A. thaliana (119 Mb; ARATH) chromosomes, reconstructing correctly all chromosomes always for CANPA and nearly always for ARATH (Figures in dropbox, CANPA table, ARATH table).
Runs took ~0.5 min for CANPA on 4 CPUs and ~2 min for ARATH on 16 CPUs.

Important remarks:

Reduce your assembly before (fasta2homozygous.py) as any redundancy will likely break the synteny.
pyScaf works better with contigs than scaffolds, as scaffolds are often affected by mis-assemblies (no de novo assembler / scaffolder is perfect...), which breaks synteny.
pyScaf works very well if divergence between reference genome and assembled contigs is below 20% at nucleotide level.
pyScaf deals with large rearrangements ie. deletions, insertion, inversions, translocations. Note however, this is experimental implementation!
Consider closing gaps after scaffolding.

Address of the bookmark: https://github.com/lpryszcz/pyScaf

RACA: Reference-Assisted Chromosome Assembly

Priya Singh — Wed, 06 Apr 2016 09:29:50 -0500

Rreference-Assisted Chromosome Assembly (RACA), an algorithm to reliably order and orient sequence scaffolds generated by NGS and assemblers into longer chromosomal fragments using comparative genome information and paired-end reads.

http://www.ncbi.nlm.nih.gov/pubmed/23307812

http://bioen-compbio.bioen.illinois.edu/RACA/

Address of the bookmark: http://bioen-compbio.bioen.illinois.edu/RACA/

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/

GAEMR

Jit — Tue, 14 Jun 2016 06:18:37 -0500

The Genome Assembly Evaluation Metrics and Reporting (GAEMR) package is an assembly analysis framework composed a number of integrated modules. These modules can be executed as a single program to generate a complete analysis report, or executed individually to generate specific charts and tables. GAEMR standardizes input by converting a variety of read types to Binary Alignment Map (BAM) format, allowing a single input format to be entered into GAEMR’s analysis pipeline, hence enabling the generation of standard reports.

GAEMR’s analysis philosophy is centered on contiguity, correctness, and completeness -- how many pieces in an assembly composed of, how well those pieces accurately represent the genome sequenced, and how much of that genome is represented by those pieces. By performing over twenty different analyses based on these principles, GAEMR gives a clear picture of the condition of a genome assembly.

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

SWALO

Jit — Wed, 30 Nov 2016 05:06:05 -0600

SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.

Download

Git repository of SWALO is at https://github.com/atifrahman/SWALO.

Address of the bookmark: https://atifrahman.github.io/SWALO/

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

sockeye

Jit — Fri, 17 Feb 2017 08:51:16 -0600

This sockeye software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3

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/