Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

Publication:

• L. Salmela, R. Walve, E. Rivals, and E. Ukkonen: Accurate selfcorrection of errors in long reads using de Bruijn graphs. Accepted to RECOMB-Seq 2016.

Download:

• LoRMA 0.3 source files
• README

Address of the bookmark: https://www.cs.helsinki.fi/u/lmsalmel/LoRMA/

Karnal -132001 (Haryana)

A walk-in-Interview is proposed to be held at National Bureau of Animal Genetic Resources, Karnal (Haryana)-132001 at 10:30 AM on 05.09.2016 for the selection of Three Research Associate & One Young Professional - II as per details given below:

Name of the Scheme / Project: Center for Agricultural Bioinformatics. The post duration is Upto 31.032017 or earlier & Co-terminus with the project.

Research Associate (Three posts)

Date & Time of Interview: 10.30 A.M. on 05.09.2016

Essential Qualifications: PhD degree in any one of discipline/Subject Biotechnology/ Animal Genetics and Breeding/ Biochemistry/ Bioinformatics/Molecular Genetics OR Master’s degree in any one of above mentioned discipline/Subject with 4 years/5 years of Bachelor’s degree having 1st division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from Fellowship/Associateship

Desirable Qualifications: Experience in Database/Next Generation Sequencing Data analysis for 02 RA posts or working experience in molecular biology, gene expression data analysis, SNP genotyping and sequence data analysis, functional gene characterization for 01 RA post.

Young Professionals II One position

Date & Time of Interview: 10.30 A.M. on 05.09.2016

Essential: B. Tech or M.Tech. in Bioinformatics / Computer Science / Computer Application.

Desirable: Experience in Linux, MySQL, Java, C++/ PHP/ PERL R based data analysis and application development in Bioinformatics.

More Info : http://14.139.252.116/ADvertisementforCabinScheme.pdf

We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set.

When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.

Availability: The method is publicly available athttp://research.haifa.ac.il/~ssagi/software/nearHGT.zip

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408

Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/

Juicebox can now be used to visualize and interactively (re)assemble genomes. Check out the Juicebox Assembly Tools Module website https://aidenlab.org/assembly for more details on how to use Juicebox for assembly.

GUI at https://aidenlab.org/juicebox/

Address of the bookmark: https://github.com/aidenlab/Juicebox

Applications are invited for a post in DST, India funded Project entitled: "Positive and negative impacts of macromolecular crowding agents during target site location by DNA binding proteins – origin of optimal search at physiological ionic concentration (Reference Number: ECR/2016/000188) ''. The selected candidate will be appointed purely on temporary basis, initially for two years as a JRF that may be extended to one year of SRF based on the performance.

Position: Junior Research Fellow (1)

Qualifications & Experience: Candidate must have a consistently good academic record with at least 60% marks in all throughout and must have qualified NET/GATE.

Desirable: Basic knowledge in the field of biophysics, molecular simulations and computational biology are desirable.

Salary: Consolidated Rs. 25,000 per month.

Tenure: The project duration is for three years and the selected candidate would be appointed after an interview. Appointment will be purely on temporary basis as stipulated by the existing rules of the University.

Interested candidates need to send an application to the address mentioned below mentioning the name of the project and post applied for (on the cover of the envelope).

The applications along with CV should be mailed at the address given below. Name, address, contact number and e. mail address of two referees must be enclosed with the application. The last date for the application is July 31st 2016.

Dr. Arnab Bhattacharjee (Principal Investigator)
Assistant Professor
School of Computational and Integrative Sciences
Jawaharlal Nehru University
New Delhi-110067
E-mail: arnab@jnu.ac.in

Note: 1. Only shortlisted candidates will be communicated to appear in the interview at SCIS, JNU and no other communications in this regard will be entertained.

2. No TA/DA will be paid for appearing in interview.

https://academic.oup.com/bioinformatics/article/35/12/2153/5162749

Address of the bookmark: https://github.com/arzwa/wgd