Difference between revisions of "MMSEQ"
Moskalenko (talk | contribs) m (Text replacement - "#uppercase" to "uc") |
|||
| Line 20: | Line 20: | ||
The MMSEQ package contains a collection of statistical tools for analysing RNA-seq expression data. Expression levels are inferred for each transcript using the mmseq program by modelling mappings of reads or read pairs (fragments) to sets of transcripts. These transcripts can be based on reference, custom or haplotype-specific sequences. The latter allows haplotype-specific analysis, which is useful in studies of allelic imbalance. The posterior distributions of the expression parameters for groups of transcripts belonging to the same gene are aggregated to provide gene-level expression estimates. Other aggregations (e.g. of transcripts sharing the same UTRs) are also possible. Isoform usage (i.e., the proportion of a gene's expression due to each isoform) is also estimated. Uncertainty in expression levels is summarised as the standard deviation of the posterior distribution of each expression parameter. When the uncertainty is large in all samples, a collapsing algorithm can be used for grouping transcripts into inferential units with reduced levels of uncertainty. | The MMSEQ package contains a collection of statistical tools for analysing RNA-seq expression data. Expression levels are inferred for each transcript using the mmseq program by modelling mappings of reads or read pairs (fragments) to sets of transcripts. These transcripts can be based on reference, custom or haplotype-specific sequences. The latter allows haplotype-specific analysis, which is useful in studies of allelic imbalance. The posterior distributions of the expression parameters for groups of transcripts belonging to the same gene are aggregated to provide gene-level expression estimates. Other aggregations (e.g. of transcripts sharing the same UTRs) are also possible. Isoform usage (i.e., the proportion of a gene's expression due to each isoform) is also estimated. Uncertainty in expression levels is summarised as the standard deviation of the posterior distribution of each expression parameter. When the uncertainty is large in all samples, a collapsing algorithm can be used for grouping transcripts into inferential units with reduced levels of uncertainty. | ||
<!--Modules--> | <!--Modules--> | ||
| − | == | + | ==Environment Modules== |
| − | + | Run <code>module spider {{#var:app}}</code> to find out what environment modules are available for this application. | |
| − | |||
| − | < | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
==System Variables== | ==System Variables== | ||
| − | * HPC_{{uc:{{#var:app}}}}_DIR | + | * HPC_{{uc:{{#var:app}}}}_DIR - installation directory |
<!--Configuration--> | <!--Configuration--> | ||
{{#if: {{#var: conf}}|==Configuration== | {{#if: {{#var: conf}}|==Configuration== | ||
| Line 67: | Line 57: | ||
<!--Turn the Table of Contents and Edit paragraph links ON/OFF--> | <!--Turn the Table of Contents and Edit paragraph links ON/OFF--> | ||
__NOTOC____NOEDITSECTION__ | __NOTOC____NOEDITSECTION__ | ||
| − | |||
| − | |||
Revision as of 13:39, 13 June 2022
Description
The MMSEQ package contains a collection of statistical tools for analysing RNA-seq expression data. Expression levels are inferred for each transcript using the mmseq program by modelling mappings of reads or read pairs (fragments) to sets of transcripts. These transcripts can be based on reference, custom or haplotype-specific sequences. The latter allows haplotype-specific analysis, which is useful in studies of allelic imbalance. The posterior distributions of the expression parameters for groups of transcripts belonging to the same gene are aggregated to provide gene-level expression estimates. Other aggregations (e.g. of transcripts sharing the same UTRs) are also possible. Isoform usage (i.e., the proportion of a gene's expression due to each isoform) is also estimated. Uncertainty in expression levels is summarised as the standard deviation of the posterior distribution of each expression parameter. When the uncertainty is large in all samples, a collapsing algorithm can be used for grouping transcripts into inferential units with reduced levels of uncertainty.
Environment Modules
Run module spider mmseq to find out what environment modules are available for this application.
System Variables
- HPC_MMSEQ_DIR - installation directory