BOL: Related items

CrossMap

Abhimanyu Singh — Mon, 05 Sep 2016 04:07:38 -0500

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)).
It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.
CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome.
We do not recommend using CrossMap to convert genome coordinates between species.

Address of the bookmark: http://crossmap.sourceforge.net/

Useful Bioinformatics Tools

Poonam Mahapatra — Mon, 29 Aug 2016 04:08:12 -0500

Collections of few handy tools for bioinformatician

http://molbiol-tools.ca/Convert.htm

Address of the bookmark: http://molbiol-tools.ca/Convert.htm

Artemis Comparison Tool (ACT)

Shruti Paniwala — Wed, 07 Sep 2016 03:54:41 -0500

ACT is a Java application for displaying pairwise comparisons between two or more DNA sequences. It can be used to identify and analyse regions of similarity and difference between genomes and to explore conservation of synteny, in the context of the entire sequences and their annotation. It can read complete EMBL, GENBANK and GFF entries or sequences in FASTA or raw format.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/artemis-comparison-tool-act

NGS Tutorial

Jit — Mon, 05 Sep 2016 09:50:46 -0500

These tutorials are written for hundreds of bioinformaticians trying to cope with large volume of next-generation sequencing (NGS) data. NGS technologies brought a dramatic shift in the world of sequencing. Merely five years back, genome sequencing of higher eukaryotes used to be very expensive endeavor. To get a genome of interest sequenced, hundreds of scientists had to raise funds together by writing a joint white-paper and petitioning to various government agencies. The tasks of sequencing and assembly were handled by dedicated sequencing facilities, of which only a few existed around the globe. Naturally, the capacities at those sequencing facilities were significantly constrained from high volume of requests

Address of the bookmark: http://www.homolog.us/Tutorials/index.php

OPERA : Optimal Paired-End Read Assembler

Jit — Fri, 09 Sep 2016 05:28:58 -0500

OPERA (Optimal Paired-End Read Assembler) is a sequence assembly program (http://en.wikipedia.org/wiki/Sequence_assembly). It uses information from paired-end/mate-pair/long reads to order and orient the intermediate contigs/scaffolds assembled in a genome assembly project, in a process known as Scaffolding. OPERA is based on an exact algorithm that is guaranteed to minimize the discordance of scaffolds with the information provided by the paired-end/mate-pair/long reads (for further details see Gao et al, 2011).

Note that since the original publication, we have made significant changes to OPERA (v1.0 onwards) including refinements to its basic algorithm (to reduce local errors, improve efficiency etc.) and incorporated features that are important for scaffolding large genomes (multi-library support, better repeat-handling etc.), in addition to other scalability and usability improvements (bam and gzip support, smaller memory footprint). We therefore encourage you to download and use our latest version: OPERA-LG. In our benchmarks, it has significantly improved corrected N50 and reduced the number of scaffolding errors. Furthermore, our latest release contains the wrapper script OPERA-long-read that enables scaffolding with long-reads from third-generation sequencing technologies (PacBio or Oxford Nanopore). The manuscript describing the new features and algorithms is available at Genome Biology. We look forward to getting your feedback to improve it further.

Address of the bookmark: https://sourceforge.net/p/operasf/wiki/The%20OPERA%20wiki/

Nanopolis: polish a genome assembly

Rahul Nayak — Thu, 26 Jul 2018 04:51:28 -0500

Software package for signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more (see Nanopolish modules, below).

Quickstart

http://nanopolish.readthedocs.io/en/latest/quickstart_consensus.html

Algorithms

http://simpsonlab.github.io/2017/06/30/nanopolish-v0.7.0/

Address of the bookmark: https://github.com/jts/nanopolish

GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach

Jit — Sat, 01 Oct 2016 15:15:29 -0500

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Address of the bookmark: http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html

PHYMMBL

Jit — Mon, 10 Oct 2016 08:56:34 -0500

Metagenomics sequencing projects collect samples of DNA from uncharacterized environments that may contain hundreds or even thousands of species. One of the main challenges in analyzing a metagenome is phylogenetic classification of raw sequence reads into groups representing the same or similar species. Such classification is a useful prerequisite for genome assembly and for analysis of the biological diversity present in a sample. The newest sequencing technologies have simultaneously made metagenomics easier, by making the sequencing process faster, and more difficult, by producing shorter read lengths than previous technologies. Methods for classifying sequences as short as 100 base pairs (bp) have until now been relatively inaccurate, requiring metagenomics projects to use older, long-read technologies. Phymm, a new classification approach for metagenomics data which uses interpolated Markov models (IMMs) to taxonomically classify DNA sequences, can accurately classify reads as short as 100 bp. Its accuracy for short reads represents a significant leap forward over previous composition-based classification methods. PhymmBL (rhymes with "thimble"), the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Address of the bookmark: http://www.cbcb.umd.edu/software/phymm/

Shinyheatmap

Jit — Fri, 21 Oct 2016 05:12:11 -0500

Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463

Address of the bookmark: http://shinyheatmap.com/

CANU genome assembly parameters !

Rahul Nayak — Mon, 07 Jan 2019 08:40:37 -0600

Choose the appropriate parameters to run Canu and run it. The assembly will take about an hour. You can use two cores (parameter -maxThreads=2) and you would like to disable cluster option, since we compute on a single Amazon server set off the option to compute on cluster useGrid=false. This specifications should be for your project discussed with a local computing guru. The parameters that are in square brackets [] are optional, symbol | stands for "or".

usage:   canu [-correct | -trim | -assemble | -trim-assemble] \
              [-s ] \
               -p  \
               -d  \
               genomeSize=[g|m|k] \
               -maxThreads=2 \
               useGrid=false \
              [other-options] \
               read_file.fastq.gz

A default Canu run produces usually high quality assembly, example of a command that was used for testing can be found below. However, there are still a lot of parameters that are possible to tweak. For example if we desire to assemble haplotypes separately of if we want to smash them together, we can alternate the error correction process.

canu -p test_asmbl \
     -d asm_test3 \
     genomeSize=2m \
     -maxThreads=2 useGrid=false \
     -pacbio-raw \ ~/pacbio/dna/sample_reads.fastq.gz

There is a brilliant section in documentation about parameter tweaking.

The output directory contains will contain many files. The most interesting ones are:

*.correctedReads.fasta.gz : file containing the input sequences after correction, trim and split based on consensus evidence.
*.trimmedReads.fastq : file containing the sequences after correction and final trimming
*.layout : file containing informations about read inclusion in the final assembly
*.gfa : file containing the assembly graph by Canu
*.contigs.fasta : file containing everything that could be assembled and is part of the primary assembly

The basic stats of assembly can be read from reports generated by the assembler, or calculated using standard UNIX command line tools.

More at https://canu.readthedocs.io/en/latest/faq.html