Difference between revisions of "PsiCLASS"

Latest revision as of 15:00, 14 December 2022

Description

PsiCLASS is a reference-based transcriptome assembler for single or multiple RNA-seq samples. Unlike conventional methods that analyze each sample separately and then merge the outcomes to create a unified set of meta-annotations, PsiCLASS takes a multi-sample approach, simultaneously analyzing all RNA-seq data sets in an experiment. PsiCLASS is both a transcript assembler and a meta-assembler, producing separate transcript sets for the individual samples and a unified set of meta-annotations. The algorithmic underpinnings of PsiCLASS include using a global subexon splice graph, statistical cross-sample feature (intron, subexon) selection methods, and an efficient dynamic programming algorithm to select a subset of transcripts from among those encoded in the graph, based on the read support in each sample.
Lastly, the set of meta-annotations is selected from among the transcripts generated for individual samples by voting. While PsiCLASS is highly accurate and efficient for medium-to-large collections of RNA-seq data, its accuracy is equally high for small RNA-seq data sets (2-10 samples) and is competitive to reference methods for single samples. Additionally, its performance is robust with the aggregation method used, including the built-in voting and assembly-based approaches such as StringTie-merge and TACO. Therefore, it can be effectively used as a multi-sample and as a single-sample assembler, as well as in conventional assemble-and-merge protocols.

Environment Modules

Run module spider psiclass to find out what environment modules are available for this application.

System Variables

HPC_PSICLASS_DIR - installation directory
HPC_PSICLASS_BIN - executable directory
HPC_PSICLASS_EXE - example directory

Citation

If you publish research that uses psiclass you have to cite it as follows:

Song, L., Sabunciyan, S., Yang, G. and Florea, L. A multi-sample approach increases the accuracy of transcript assembly. Nat Commun 10, 5000 (2019)

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 {{App_Description|app={{#var:app}}|url={{#var:url}}|name={{#var:app}}}}|}}
-PsiCLASS is a reference-based transcriptome assembler for single or multiple RNA-seq samples. Unlike conventional methods that analyze each sample separately and then merge the outcomes to create a unified set of meta-annotations, PsiCLASS takes a multi-sample approach, simultaneously analyzing all RNA-seq data sets in an experiment. PsiCLASS is both a transcript assembler and a meta-assembler, producing separate transcript sets for the individual samples and a unified set of meta-annotations. The algorithmic underpinnings of PsiCLASS include using a global subexon splice graph, statistical cross-sample feature (intron, subexon) selection methods, and an efficient dynamic programming algorithm to select a subset of transcripts from among those encoded in the graph, based on the read support in each sample. Lastly, the set of meta-annotations is selected from among the transcripts generated for individual samples by voting. While PsiCLASS is highly accurate and efficient for medium-to-large collections of RNA-seq data, its accuracy is equally high for small RNA-seq data sets (2-10 samples) and is competitive to reference methods for single samples. Additionally, its performance is robust with the aggregation method used, including the built-in voting and assembly-based approaches such as StringTie-merge and TACO. Therefore, it can be effectively used as a multi-sample and as a single-sample assembler, as well as in conventional assemble-and-merge protocols.
+PsiCLASS is a reference-based transcriptome assembler for single or multiple RNA-seq samples. Unlike conventional methods that analyze each sample separately and then merge the outcomes to create a unified set of meta-annotations, PsiCLASS takes a multi-sample approach, simultaneously analyzing all RNA-seq data sets in an experiment. PsiCLASS is both a transcript assembler and a meta-assembler, producing separate transcript sets for the individual samples and a unified set of meta-annotations. The algorithmic underpinnings of PsiCLASS include using a global subexon splice graph, statistical cross-sample feature (intron, subexon) selection methods, and an efficient dynamic programming algorithm to select a subset of transcripts from among those encoded in the graph, based on the read support in each sample. <br>
+Lastly, the set of meta-annotations is selected from among the transcripts generated for individual samples by voting. While PsiCLASS is highly accurate and efficient for medium-to-large collections of RNA-seq data, its accuracy is equally high for small RNA-seq data sets (2-10 samples) and is competitive to reference methods for single samples. Additionally, its performance is robust with the aggregation method used, including the built-in voting and assembly-based approaches such as StringTie-merge and TACO. Therefore, it can be effectively used as a multi-sample and as a single-sample assembler, as well as in conventional assemble-and-merge protocols.
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