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What can RSEM be used for in RNA Seq?
RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation.
What can RSEM package do for parallel computation?
The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels.
How to prepare a reference sequence for RSEM?
To prepare the reference sequences, you should run the rsem-prepare-reference program. Run to get usage information or visit the rsem-prepare-reference documentation page. RefSeq and Ensembl are two frequently used annotations. For human and mouse, GENCODE annotaions are also available.
What’s the unique feature of the RSEM tool?
The unique feature of RSEM is, the read depth plots can be stacked, with read depth contributed to unique reads shown in black and contributed to multi-reads shown in red. In addition, models learned from data can also be visualized. Last but not least, RSEM contains a simulator.
Are there any scripts that don’t install ebseq?
Notethat make installdoes not install EBSeqrelated scripts, such as rsem-generate-ngvector, rsem-run-ebseq, and rsem-control-fdr. But rsem-generate-data-matrix, which generates count matrix for differential expression analysis, is installed.
What are the prequisites for using RSEM in Python?
But rsem-generate-data-matrix, which generates count matrix for differential expression analysis, is installed. Prerequisites C++, Perl and R are required to be installed. To use the –gff3option of rsem-prepare-reference, Python is also required to be installed.