The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

In fact, there are huge demands for expert biological data analyst. It’s a fairly new  (not exactly) hot area, these bioinformatician are invaluable because they know and understand the significance of biological data for your research and how you can use it for better understanding of biological problems.

The bioinformatics can discover biological patterns and stories in genomic and proteomics data. They can develop the pipeline needed to properly collect, store and analyse it.

Once your research group is ready to make a larger investment and hire a bioinformatician to gain a competitive edge, there are several key traits to seek out in potential candidates. The best bioinformatician are:

1. Highly Skilled - programming skills, experience with the biological software and tools.

The biological data won’t illuminate much if the scientist analysing it doesn’t possess practical programming skills, experience with the biological software and tools and a thorough understanding of basic biological stuff. A solid background in mathematics and statistics is also an indispensable trait.

2. Insight - Real vision, robust understanding and deep insight.

In order to hire the best bioinformatics and computational biologist scientist for your needs, it is always recommended and mostly practiced by the recruiters, to ask each contender to write and develop a sample script/presentation based on a specific set of data you provide. Then, explore the approaches used to deal with data provided and pick up those candidates who convey real vision, robust understanding and deep insight.

3. Energetic – Curiosity to explore

Mostly natural curiosity and enthusiasm for solving big biological problems coupled with an ability to transform data into a scientific stories may place one candidate above the rest. In addition to achieve that, the bioinformatician should be agile enough to quickly modify their methods to suit changes within a particular research.

4. Researcher – Publications

Look for someone who has a keen sense and understanding of concern biological problems. You can judge it by looking at previously published papers and data. It is always recommended to have a look at GitHub and other repository for codes written by her/him.

5. Impressive communicator - Insight that can’t be expressed is worthless.

Good bioinformatics scientists are able to uncover biological patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. In other words, they should must have good scientific writing skills. A computational biologis/bioinformatician  should know how to present the data and tell a scientific story through numbers/images.

• Inchworm assembles the RNA-seq data into the unique sequences of transcripts, often generating full-length transcripts for a dominant isoform, but then reports just the unique portions of alternatively spliced transcripts.

• Chrysalis clusters the Inchworm contigs into clusters and constructs complete de Bruijn graphs for each cluster. Each cluster represents the full transcriptonal complexity for a given gene (or sets of genes that share sequences in common). Chrysalis then partitions the full read set among these disjoint graphs.

• Butterfly then processes the individual graphs in parallel, tracing the paths that reads and pairs of reads take within the graph, ultimately reporting full-length transcripts for alternatively spliced isoforms, and teasing apart transcripts that corresponds to paralogous genes.

More at https://github.com/trinityrnaseq/trinityrnaseq/wiki

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Download Trinity here.

Build Trinity by typing 'make' in the base installation directory.

Assemble RNA-Seq data like so:

 Trinity --seqType fq --left reads_1.fq --right reads_2.fq --CPU 6 --max_memory 20G 

Find assembled transcripts as: 'trinity_out_dir/Trinity.fasta'

Address of the bookmark: https://github.com/trinityrnaseq/trinityrnaseq/wiki

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 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/

Canu is a hierachical assembly pipeline which runs in four steps:

• Detect overlaps in high-noise sequences using MHAP
• Generate corrected sequence consensus
• Trim corrected sequences
• Assemble trimmed corrected sequences

Read the documentation

New release https://github.com/marbl/canu/releases

Address of the bookmark: https://github.com/marbl/canu

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

The pipeline consists of three steps/modules:

• redundancy reduction: detection and selectively removal of redundant contigs from an initial de novo assembly
• scaffolding: joining of genome fragments using paired-end and/or mate-pairs reads
• gap closing

Redundans is:

• fast & lightweight, multi-core support and memory-optimised, so it can be run even on the laptop for small-to-medium size genomes
• flexible toward many sequencing technologies (Illumina, 454 or Sanger) and library types (paired-end, mate pairs, fosmids)
• modular: every step can be ommited or replaced by another tools

Address of the bookmark: https://github.com/Gabaldonlab/redundans

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

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