BOL: Related items

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/

HybPiper

Jit — Fri, 04 Nov 2016 05:02:10 -0500

HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.

Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.

HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information, please see our wiki.

HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.

Address of the bookmark: https://github.com/mossmatters/HybPiper

R Graphical Cookbook by Winston Chang

Abhimanyu Singh — Fri, 04 Nov 2016 12:50:30 -0500

R Graphical Cookbook by Winston Chang

A very nice book by Winston Chang for R ethusiast. The R code presented in these pages is the R code actually used to produce the Figures in the book. There will be differences compared to the code chunks shown in the text of the book, but in most cases the differences will be that these pages contain additional code to lay out multiple plots on a single "page".

The code presented for each figure is self-contained, i.e., all code required to produce the figure is included. This means that there is sometimes considerable overlap of code between several figures In some cases, it may be necessary to install an add-on package from CRAN to get the code to run.

More books at http://www.e-reading.club/bookreader.php/137370/C486x_APPb.pdf

fqtools

Jit — Thu, 08 Dec 2016 09:31:12 -0600

fqtools is a software suite for fast processing of FASTQ files. Various file manipulations are supported. See below for a full list of the subcommands available and a brief description of their purpose. Most of the individual subcommands will take either a single file or a pair of files as input. If no input file is specified, fqtools will attempt to read data from stdin. In this case, it is advisabe to specify the format of the data provided. For subcommands that generate FASTQ data, either a single file or a pair of files will be generated. If no -o argument is provided, single files will be writted to stdout.

Address of the bookmark: https://github.com/alastair-droop/fqtools

BIMA V3: an aligner customized for mate pair library sequencing

Abhimanyu Singh — Wed, 14 Dec 2016 15:20:00 -0600

Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate pair read pairs to a reference genome is a challenging and
time consuming process for most NGS alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.), and presence of structural variant breakpoints within reads increases mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular NGS alignment programs when processing mate pair sequencing.
Availability: http://bioinformaticstools.mayo.edu/research/bima/
Contact: vasmatzis.george@mayo.edu

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf

LAST

Bulbul — Mon, 19 Dec 2016 14:07:53 -0600

LAST can:

Handle big sequence data, e.g:
- Compare two vertebrate genomes
- Align billions of DNA reads to a genome
Indicate the reliability of each aligned column.
Use sequence quality data properly.
Compare DNA to proteins, with frameshifts.
Compare PSSMs to sequences
Calculate the likelihood of chance similarities between random sequences.
Do split and spliced alignment.
Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

Address of the bookmark: http://last.cbrc.jp/

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