Using Drosophila melanogaster spike-in data to refine diferential expression algorithms.
2015/03/12 - HOZ
Detection of differential expression from de-novo RNASeq data.
A common bioinformatics problem is the detection of differential expression in two samples. When the data is from an RNASeq experiment the procedure is usually to map reads to a reference genome, e.g. using bowtie[1]. When no reference genome exists, a de-novo assembly is generated from the reads first[2], then the reads are mapped again to the artificial reference produced by concatenating the contigs from the de-novo assembly.
K-mer counting is a pre-processing step that is used prior to de-novo assembly to normalize the sequence data and remove some errors [3].
An alternative strategy could be to create a de Bruijn graph where the edges have an associated count of occurrences in the two samples. The full graph can be examined to detect regions of differential expression, and only those contigs assembled.
For the summer project, the student can use simulated reads, like those generated with flux-simulator[4], to detect differential expression. Once the software pipeline is designed, we can apply the pipeline to a spike-in experiment with known concentrations of Drosophila and foreign RNA, as described in [5].
At the UPR, several groups are doing de-novo RNAseq in non-model organisms, and this project could provide tools to help these projects.
[1] http://genomebiology.com/2009/10/3/R25 [2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571712/ [3] http://arxiv.org/abs/1203.4802 [4] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3488205/ [5] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166838/
2014/09/09 - HOZ
We have a single repetition of 5% and 1%, but a bunch of 2.5%
5% ERCC in dmel S2
GSM517059 100ng_S2_5ng_ERCC_phaseV_pool15_mRNA-seq
2.5% ERCC in dmel S2
GSM517060 100ng_S2_2.5ng_ERCC_phaseV_pool15_mRNA-seq
GSM516588 100ng_library_methA_S2_2.5%ERCC_phaseV_pool15mRNA GSM516589 100ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA GSM516590 50ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA GSM516591 10ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA GSM516592 1ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA GSM516593 0.4ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA GSM516594 0.01ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA
1% ERCC in dmel S2
GSM517061 100ng_S2_1ng_ERCC_phaseV_pool15_mRNA
2015/05/12 - HOZ
SRA links for GSE20579
http://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=125273
GSM517059: 100ng_S2_5ng_ERCC_phaseV_pool15_mRNA-seq - SRX019234
http://www.ncbi.nlm.nih.gov/sra/SRX019234%5Baccn%5D
ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR039/SRR039933/SRR039933.sra
GSM517061: 100ng_S2_1ng_ERCC_phaseV_pool15_mRNA - SRX019236
http://www.ncbi.nlm.nih.gov/sra/SRX019236%5Baccn%5D
ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR039/SRR039935/SRR039935.sra
Bioproject for GSE20555
http://www.ncbi.nlm.nih.gov/bioproject/?term=GSE20555
SRA links for GSM16588-94 are here:
http://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&from_uid=125199
GSM516588 - 100ng_library_methA_S2_2.5%ERCC_phaseV_pool15mRNA - SRX018870
http://www.ncbi.nlm.nih.gov/sra/SRX018870%5Baccn%5D
ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR039/SRR039458/SRR039458.sra
GSM516590 - 50ng_library_methB_S2_2.5%ERCC_phaseV_pool15_mRNA - SRX018872
ftp://ftp-trace.ncbi.nlm.nih.gov/sra/sra-instant/reads/ByRun/sra/SRR/SRR039/SRR039460/SRR039460.sra
http://www.ncbi.nlm.nih.gov/bioproject/?term=GSE20579
The 4 ftp files are SRA files for 5%, 1%, 2.5%, 2.5% ERCC spike ins.
The files can be read (and dumped to fasta/fastq format) with the SRA toolkit:
http://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/current/
$ grep "^ftp" README.md > getit
$ wget -ci getit