BOL

A probabilistic framework for structural variant discovery. Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6):...

Recent technological progress has greatly facilitated de novo genome sequencing. However, de novo assemblies consist in many pieces of contiguous sequence (contigs) arranged in thousands of scaffolds instead of small numbers of...

Genome basic stats http://www.cbs.dtu.dk/courses/genomics_course/Day3.pdf #Genome #Stats #Plot #R

Faster, more accurate algorithms function prediction "GeneMANIA (Multiple Association Network Integration Algorithm)" have however been developed in recent years and are publicly available on the web, indicating the future direction of function...

du -sh * will give you the size of all the directories,files etc in current directory in human readable format. #Linux #Size #Tricks #Folders #Files

Most variant calling pipelines result in files containing large quantities of variant information. The variant call format (vcf) is an increasingly popular format for this data. The format of these files and their content is discussed in...

NGS exercise http://wiki.bits.vib.be/index.php/NGS_Exercise.5 #NGS #VIB #Training #Exercise

We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the...

RepeatModeler is a de-novo repeat family identification and modeling package. At the heart of RepeatModeler are two de-novo repeat finding programs ( RECON and RepeatScout ) which employ complementary computational methods for identifying repeat...

_A5-miseq_ is a pipeline for assembling DNA sequence data generated on the Illumina sequencing platform. This README will take you through the steps necessary for running _A5-miseq_. Point to note: There are many situations where A5-miseq is not...

I guess, it will be easier to "split by middle". My suggestions: Count the total number of reads -> use head or tail -n #num command to extract. 

I would like to extract a subset of PE reads (50%) and store them in seperate files. It should be in both way "split by middle" or "random". Is there any way to achieve it?