BOL: Related items

JCVI:Python utility libraries on genome assembly, annotation and comparative genomics

Jit — Tue, 17 Mar 2020 06:19:06 -0500

Collection of Python libraries to parse bioinformatics files, or perform computation related to assembly, annotation, and comparative genomics.

https://github.com/tanghaibao/jcvi

More at https://github.com/tanghaibao/jcvi/wiki

Address of the bookmark: https://github.com/tanghaibao/jcvi

Sample bandage input file for visual analysis

Jit — Wed, 06 Jan 2021 03:51:50 -0600

Sample bandage input file for visual analysis ...

HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding.

Jit — Mon, 26 Oct 2020 21:23:36 -0500

Despite marked recent improvements in long-read sequencing technology, the assembly of diploid genomes remains a difficult task. A major obstacle is distinguishing between alternative contigs that represent highly heterozygous regions. If primary and secondary contigs are not properly identified, the primary assembly will overrepresent both the size and complexity of the genome, which complicates downstream analysis such as scaffolding.

More at https://github.com/esolares/HapSolo

Address of the bookmark: https://github.com/esolares/HapSolo

Professor (all levels) in Bioinformatics and Computational Biology

Tue, 22 Nov 2016 05:43:38 -0600

King Abdullah University of Science and Technology (KAUST) (kaust.edu.sa) is seeking a highly motivated and skilled faculty member for the Bioinformatics track whose research focuses on development of methods and tools for Bioinformatics and Computational Biology.
KAUST is an international, graduate-level research university dedicated to advancing science and technology through interdisciplinary research, education, and innovation. Located on the shores of the Red Sea in Saudi Arabia, KAUST offers superb research facilities, generous assured research funding, and internationally competitive salaries, attracting top international faculty, scientists, engineers, and students to conduct fundamental and goal-oriented research to address the world’s pressing scientific and technological challenges in the areas of food, water, energy, and the environment.
The successful applicant is expected to develop world-leading research in domain of bioinformatics/computational biology with focus on development of novel computational approaches for efficient and accurate methods of analyzing biological phenomena at molecular level. The faculty member will be part of the Computational Bioscience Research Center (CBRC) within the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. The position will remain open until filled.

Requirements:

PhD or equivalent in a Computer Science, Mathematics or Engineering discipline. Candidates should be well-established within the research field relevant to the position grade. They should demonstrate original research and experience at the highest international level.

Responsibilities and tasks:

Research competence in the following areas is preferred:
Analysis of next generation sequencing (NGS) and other ‘omics’ data (e.g. CAGE, ChIP-Seq, DHS, RNA-Seq, Ribo-Seq, proteomic, metabolic and NMR spectra, etc.).
Signaling, regulatory and metabolic pathways analysis.
Development of tools (web-based and standalone) suited for efficient computational biology/bioinformatics.

Visit cemse.kaust.edu.sa to apply.

HapSolo: An optimization approach for removing secondary haplotigs during diploid genome assembly and scaffolding

Jit — Sat, 08 May 2021 21:25:00 -0500

HapSolo, that identifies secondary contigs and defines a primary assembly based on multiple pairwise contig alignment metrics. HapSolo evaluates candidate primary assemblies using BUSCO scores and then distinguishes among candidate assemblies using a cost function. The cost function can be defined by the user but by default considers the number of missing, duplicated and single BUSCO genes within the assembly. HapSolo performs hill climbing to minimize cost over thousands of candidate assemblies.

Address of the bookmark: https://github.com/esolares/HapSolo

EXCAVATOR2tool

Bulbul — Wed, 30 Nov 2016 04:09:19 -0600

EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.
EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it’s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.

Address of the bookmark: https://sourceforge.net/projects/excavator2tool/

Illumina based assembly pipeline steps !

Surabhi Chaudhary — Fri, 10 Dec 2021 06:22:54 -0600

Illumina

Merge re-sequenced FastQ files (cat)
Read QC (FastQC)
Adapter trimming (fastp)
Removal of host reads (Kraken 2; optional)
Variant calling
1. Read alignment (Bowtie 2)
2. Sort and index alignments (SAMtools)
3. Primer sequence removal (iVar; amplicon data only)
4. Duplicate read marking (picard; optional)
5. Alignment-level QC (picard, SAMtools)
6. Genome-wide and amplicon coverage QC plots (mosdepth)
7. Choice of multiple variant calling and consensus sequence generation routes (iVar variants and consensus; default for amplicon data || BCFTools, BEDTools; default for metagenomics data)
  - Variant annotation (SnpEff, SnpSift)
  - Consensus assessment report (QUAST)
  - Lineage analysis (Pangolin)
  - Clade assignment, mutation calling and sequence quality checks (Nextclade)
  - Individual variant screenshots with annotation tracks (ASCIIGenome)
8. Intersect variants across callers (BCFTools)
De novo assembly
1. Primer trimming (Cutadapt; amplicon data only)
2. Choice of multiple assembly tools (SPAdes || Unicycler || minia)
  - Blast to reference genome (blastn)
  - Contiguate assembly (ABACAS)
  - Assembly report (PlasmidID)
  - Assembly assessment report (QUAST)
Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)

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

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/