In this report, we compare and evaluate several genome assembly software based on TSG technology. The experimentation has been performed on 4 reference genomes and the results evaluated with the QUAST software. The 11 software that have been evaluated are: Celera Assembler , Falcon , Miniasm, Newbler , SGA Assembler, Smartdenovo, Abruijn, Ra, DBG2OLC, Spades and Cerulean. The first 8 software use only long reads, while the 3 last software can merge long and short reads

1.Stupid for biologist: Biology is so complex that it will make bioinformatician feel stupid. There are no any universal fixed rules; it can surprise you any time. So be nice to biologists who ask questions and resolve your biological puzzles. Sometime you will have no idea what the hell you were doing either.

2.Puzzling why: Do not hesitate to ask question. Especially. at the beginning of project you will have to ask a lot of questions. Instead of puzzling it out at end check out and clear your doubt even for a single error. It may can leads to wrong conclusion.

3.Running marathon: The most of the biological software’s documentation is always incomplete. In other word they are no more than 95 percent complete. Sometime a single problem can halt your entire project for months. Compilation and running the pipelines in tedious because almost all are interdependent and need proper configuration. I face the same kind of problem with Evolver :( …

4.Folders missing: The pipelines generate lots of data, and we keep them in several folders for future use. But sometime we delete them by mistake and move to recovery…

5.Digging deeper: Digging deeper is fruitful, but some time it can be catastrophic. You may get frustrated or direction less. So keep a biologist with you for rescue …. Sometime an expert computer programmer to handle your server. Remember, the server will always go down when you need it the most.

The most common frustrating  common line: Why do we do this again?

~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants

This will output a polished_variants.vcf file, that contains all the refined variants.

Sometimes it is helpful to look at the polished sequence, e.g. with the IGV browser. In that case you want SViper to output the polished and aligned sequences in a bam file via the option --output-polished-bam:

~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants --output-polished-bam

Address of the bookmark: https://github.com/smehringer/SViper

The Bioinformatics and Systems Biology Unit of Université de Liège (Belgium) is looking for a highly motivated master student with programming skills for a PhD thesis project (4 years, fully funded) with the goal of designing computational tools that use literature, genomic and structural data in order to infer regulatory and metabolic networks.

Applicants are invited to send their resume and a recommendation letter to Prof. Patrick Meyer (more details at www.biosys.ulg.ac.be )

For more information : www.biosys.ulg.ac.be

Usage: perl run_rcorrector.pl [OPTIONS]
OPTIONS:
	Required
	-s seq_files: comma separated files for single-end data sets
	-1 seq_files_left: comma separated files for the first mate in the paried-end data sets
	-2 seq_files_right: comma separated files for the second mate in the paired-end data sets
	-i seq_files_interleaved: comma sperated files for interleaved paired-end data sets
	Optional
	-k INT: kmer_length (<=32, default: 23)
	-od STRING: output_file_directory (default: ./)
	-t INT: number of threads to use (default: 1)
	-trim : allow trimming (default: false)
	-maxcorK INT: the maximum number of correction within k-bp window (default: 4)
	-wk FLOAT: the proportion of kmers that are used to estimate weak kmer count threshold, lower for more divergent genome (default: 0.95)
	-ek INT: expected number of kmers; does not affect the correctness of program but affects the memory usage (default: 100000000)
	-stdout: output the corrected reads to stdout (default: not used)
	-verbose: output some correction information to stdout (default: not used)
	-stage INT: start from which stage (default: 0)
		0-start from begining(storing kmers in bloom filter) ;
		1-start from count kmers showed up in bloom filter;
		2-start from dumping kmer counts into a jf_dump file;
		3-start from error correction.

Address of the bookmark: https://github.com/mourisl/Rcorrector/

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of over 97% strong scaling.

Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.

Contact: akiyama@cs.titech.ac.jp

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/08/06/bioinformatics.btu532.short

Here is an illustration of this pipeline:

Address of the bookmark: https://github.com/BGI-Qingdao/SLR-superscaffolder

Finally, I find the underlying R code in a file created by GrapheR. For more details read also the package vignette, which is available in English, French and German!

where, c is the count of a k-mer from reads, m is the mode of counts of read k-mers, and n is the copy of the k-mer in the assembly.

Address of the bookmark: https://github.com/liu3zhenlab/KAD

More at http://pythonhosted.org/pybedtools/

A powerful toolset for genome arithmetic.http://code.google.com/p/bedtools/