Recent Changes

HTSeq-count has three running modes (option denoted as –m or --mode=, options are: union, intersection_strict, and intersection_nonempty) that determine how it handles read data. Which mode you choose depends on how conservative you want to be and the quality of your genome annotation. “Union” is the default mode and is applicable to most datasets.
We will not try this here with our E. coli dataset in the interest of time. As an example, however, there is an ecoli .gtf file in Ecoli_sampledata/ folder called ecoli_APEC.2.gtf. This is what a typical gft file looks like. This gft file and the sorted sam input file you created are all you need to count reads with HTSeq-count.
the file):
{merge_tables (1).py}
Once we have a text file with read counts for all our genes, its time to call differential expression!

How you clean your RNAseq data will depend on what platform you use and the quality of the data that is returned to you. You should spend a little bit of time looking at the FastQC files that you receive with your data to make decisions about the best way to clean your reads. Programs like trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic), cutadapt (https://pypi.python.org/pypi/cutadapt), or the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) can all trim reads based on quality or location (i.e., the first or last 3 bases) and remove adaptor contamination.
2. Mapping to a genome or transcriptome with Tophat2
(i.e., STAR, Kallisto)Kallisto, etc.) that may
http://ccb.jhu.edu/software/tophat
TopHat2 is a splice junction mapper for RNA-Seq reads. Tophat2 uses the short-read aligner Bowtie and analyzes the resulting mapped reads to uncover splice junctions between exons. To map reads with Tophat2 you will need a genome to map to, but you will not need a reference annotation (however, having a reference annotation will make the program run substantially faster!).
HTSeq-count has three running modes (option denoted as –m or --mode=, options are: union, intersection_strict, and intersection_nonempty) that determine how it handles read data. Which mode you choose depends on how conservative you want to be and the quality of your genome annotation. “Union” is the default mode and is applicable to most datasets.
We will not try this here with our E. coli dataset in the interest of time. As an example, however, there is an ecoli .gtf file in Ecoli_sampledata/ folder called ecoli_APEC.2.gtf. This is what a typical gft file looks like. This gft file and the sorted sam input file you created are all you need to count reads with HTSeq-count.
output example. Alternatively use this python script (instructions at the top of the file):
{merge_tables (1).py}
Once we have a text file with read counts for all our genes, its time to call differential expression!
5. Choosing how you call differential expression

Overview of the pipeline
{ModelvsNonModel_Pipeline.jpg}
reference transcriptome (bowtie2 or bwa) or genome (tophat2, hisat2, STARR) to map
Note on filtering/cleaning RNAseq data:
How you clean your RNAseq data will depend on what platform you use and the quality of the data that is returned to you. You should spend a little bit of time looking at the FastQC files that you receive with your data to make decisions about the best way to clean your reads. Programs like trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic), cutadapt (https://pypi.python.org/pypi/cutadapt), or the fastx toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) can all trim reads based on quality or location (i.e., the first or last 3 bases) and remove adaptor contamination.

Part I Population Genomics
April 1, 2015
BiPlease click here:this link: Population genomics

Part I Population Genomics
April 1, 2015
Instructor: Ke Bi PopulationBiPlease click here: Population genomics {README(PartII-PopGenomics).pdf}

Part I Population Genomics
April 1, 2015
Ke Bi Population genomics {README(PartII-PopGenomics).pdf}

April 1, 2015
Instructor: Ke Bi
{README-denovoTargetCapturePopGen.pdf}

This workshop will cover key steps in analyzing 16S bacterial amplicon sequencing data. We will learn how to QC raw data, then use Mothur software to cluster OTUs and make taxonomic assignments.This tutorial reflects the way that I usually do my analyses.
Before the workshop, you need to:
Mothur software (v1.35.1)(v1.39.5) OR according to
https://github.com/mothur/mothur/releases/tag/v1.35.1
2. Download and install “FastQC” – make sure that you have Java installed too. If you have (Java) trouble, consider this as an optional installation. http://www.bioinformatics.babraham.ac.uk/projects/download.html#fastqc

WORKSHOP TUTORIAL
{16S_Amplicon_Sequencing_Data_Analysis_DL_April2016.pdf} {soil_final.taxonomy.txt} {soil_stability.meta.txt}
{soil_final.0.03.subsample.shared}