Details
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Type: Task
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Status: Closed (View Workflow)
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Priority: Major
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Resolution: Done
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Affects Version/s: None
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Fix Version/s: None
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Labels:None
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Story Points:1
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Sprint:Spring 1 2022 Jan 3 - Jan 14, Spring 2 2022 Jan 18 - Jan 28
Description
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. This is not a deal-breaker, but it would be nice if we could perform a single test even for alternative splicing choices where there are more than two options. There may be better software or better methods we can use instead.
However, one thing to always keep in mind is that we mainly care about understanding how and if splicing is regulated, and in detecting changes in the splicing machinery's function. In this case, what matters ultimately the frequency with which individual splice sites get used relative to alternative options, and what causes this frequency to change. Currently, we assess splice site selection frequency by counting relevant reads and ignoring both irrelevant reads, such as reads that align outside the differentially spliced regions, or reads we can't assign to a single choice. Moreover, because of the complexity of splicing and because of the unknowableness of reads mapping far away from a given splice site, we ought to focus our attention on the reads for which we are most confident in their mapping. What we care about the most is whether or not the underlying frequency of splice site usage changes between treatments or conditions. It's nice if we can know what that frequency is most likely to be, but a very rough estimate of the actual value is fine. Again, we care about the fact of a change, because the change signals that something biologically interesting might be happening.
For this task, let's scan the papers found in the literature review results (see: IGBF-3041) to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline?
Also, let's investigate the statistics literature to determine if the test we need is already developed for an analogous setting that we can deploy for ourselves. We already have software that counts and reports relevant read alignments (see: arabitag), and so really we only need a good way to test for differences in splicing across conditions.
Attachments
Activity
Field | Original Value | New Value |
---|---|---|
Epic Link | IGBF-3039 [ 21553 ] |
Sprint | Spring 1 2022 Jan 3 - Jan 14 [ 136 ] |
Rank | Ranked lower |
Description |
The Loraine Lab has developed an alternative splicing RNA-Seq analysis tool called "find_junctions" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. There may be better software we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. Another weakness is that the test does not take into account the number of informative reads collected. There may be better software or better methods we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? |
Description |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. Another weakness is that the test does not take into account the number of informative reads collected. There may be better software or better methods we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. Another weakness is that the test does not take into account the number of informative reads collected. There may be better software or better methods we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? Also, investigate the statistics literature to determine if the test we need is already developed. |
Description |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. Another weakness is that the test does not take into account the number of informative reads collected. There may be better software or better methods we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? Also, investigate the statistics literature to determine if the test we need is already developed. |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. Another weakness is that the test does not take into account the number of informative reads collected. There may be better software or better methods we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? Also, investigate the statistics literature to determine if the test we need is already developed for an analogous setting that we can deploy for ourselves. |
Summary | Investigate: Alternative splicing statistical analysis tools | Investigate: Alternative splicing statistical analysis |
Assignee | Ann Loraine [ aloraine ] |
Status | To-Do [ 10305 ] | In Progress [ 3 ] |
Description |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. Another weakness is that the test does not take into account the number of informative reads collected. There may be better software or better methods we can use instead. For this task, use the literature review results to identify alternative splicing analysis software packages and determine: Which of these, if any, should we explore as an alternative to the find junctions pipeline? Also, investigate the statistics literature to determine if the test we need is already developed for an analogous setting that we can deploy for ourselves. |
The Loraine Lab has developed alternative splicing RNA-Seq analysis tools called "find_junctions" and "arabitag" that we've used in conjunction with statistical testing to detect when splicing changes in an experiment.
This software works OK but interpreting results when one gene has multiple alternative splicing choices is difficult. Another potential weakness is the statistical test we use to detect a change across conditions. To test for a change, we compare proportion means using a t-test, after converting the proportions data to something more normal, using a transformation. In practice, this works OK, but things get confusing when we have multiple splicing patterns affect the same region of a gene, as we end up comparing each option pairwise against each other option. This is not a deal-breaker, but it would be nice if we could perform a single test even for alternative splicing choices where there are more than two options. There may be better software or better methods we can use instead. However, one thing to always keep in mind is that we mainly care about understanding how and if splicing is regulated, and in detecting changes in the splicing machinery's function. In this case, what matters ultimately the frequency with which individual splice sites get used relative to alternative options, and what causes this frequency to change. Currently, we assess splice site selection frequency by counting relevant reads and ignoring both irrelevant reads, such as reads that align outside the differentially spliced regions, or reads we can't assign to a single choice. Moreover, because of the complexity of splicing and because of the unknowableness of reads mapping far away from a given splice site, we ought to focus our attention on the reads for which we are most confident in their mapping. What we care about the most is whether or not the underlying frequency of splice site usage changes between treatments or conditions. It's nice if we can know what that frequency is most likely to be, but a very rough estimate of the actual value is fine. Again, we care about the fact of a change, because the change signals that something biologically interesting might be happening. For this task, let's scan the papers found in the literature review results (see: Also, let's investigate the statistics literature to determine if the test we need is already developed for an analogous setting that we can deploy for ourselves. We already have software that counts and reports relevant read alignments (see: [arabitag|https://bitbucket.org/lorainelab/altspliceanalysis]), and so really we only need a good way to test for differences in splicing across conditions. |
Attachment | Mosaic.jpeg [ 17048 ] |
Comment | [ If we care about how splicing is regulated, and in detecting changes in the splicing machinery's function, then what matters ultimately is the frequency with which individual splice sites get used relative to alternative options. We can assess splice site selection frequency by counting relevant reads and ignoring both irrelevant reads, such as reads that align the differentially spliced regions, or reads we can't assign to a single choice. Moreover, because of the complexity of splicing and because of the unknowableness of reads mapping far away from a given splice site, we ought to focus our attention on the reads for which we are most confident in their mapping. What we care about the most is whether or not the underlying frequency of splice site usage changes between treatments or conditions. It's nice if we can know what that frequency is most likely to be, but a very rough estimate of the actual value is fine. Again, we care about the fact of a change, because the change signals that something biologically interesting might be happening. ] |
Status | In Progress [ 3 ] | Needs 1st Level Review [ 10005 ] |
Assignee | Ann Loraine [ aloraine ] |
Status | Needs 1st Level Review [ 10005 ] | First Level Review in Progress [ 10301 ] |
Status | First Level Review in Progress [ 10301 ] | To-Do [ 10305 ] |
Assignee | Ann Loraine [ aloraine ] |
Status | To-Do [ 10305 ] | In Progress [ 3 ] |
Status | In Progress [ 3 ] | To-Do [ 10305 ] |
Assignee | Ann Loraine [ aloraine ] |
Status | To-Do [ 10305 ] | In Progress [ 3 ] |
Assignee | Nowlan Freese [ nfreese ] |
Attachment | 3044.xlsx [ 17068 ] |
Assignee | Nowlan Freese [ nfreese ] |
Status | In Progress [ 3 ] | Needs 1st Level Review [ 10005 ] |
Sprint | Spring 1 2022 Jan 3 - Jan 14 [ 136 ] | Spring 1 2022 Jan 3 - Jan 14, Spring 2 2022 Jan 18 - Jan 28 [ 136, 137 ] |
Rank | Ranked higher |
Status | Needs 1st Level Review [ 10005 ] | First Level Review in Progress [ 10301 ] |
Status | First Level Review in Progress [ 10301 ] | Ready for Pull Request [ 10304 ] |
Status | Ready for Pull Request [ 10304 ] | Pull Request Submitted [ 10101 ] |
Status | Pull Request Submitted [ 10101 ] | Reviewing Pull Request [ 10303 ] |
Status | Reviewing Pull Request [ 10303 ] | Merged Needs Testing [ 10002 ] |
Status | Merged Needs Testing [ 10002 ] | Post-merge Testing In Progress [ 10003 ] |
Resolution | Done [ 10000 ] | |
Status | Post-merge Testing In Progress [ 10003 ] | Closed [ 6 ] |
Assignee | Ann Loraine [ aloraine ] |
rMATs - tests whether there is a difference in proportion higher than a user-supplied threshold (the default difference is 0.05). Also, it tries to model variability in the proportions obtained from replicate samples.
It also tries to normalize for splicing event length by including a length factor in the "percent spliced in" proportion. In addition, it uses reads aligned on top of exons, not just junction reads, as counting toward the use of one splice site or another.
The statistical approach is described in a 2014 PNAS paper. I'm not sure if the paper provides a solid justification for the method, however. Simulations are presented, but the simulations assume a binary choice only, which, as we know from experience, is not realistic for many of the most interesting cases, e.g., highly alternatively spliced genes that may alter their splicing patterns under different conditions or treatments.
The current version of rMATs is called rMATS "turbo" which includes a number of speed enhancements, including some parallelization of the code.
Big question I still have is: Does it detect differential splicing among un-annotated splice variants? This matters for the tomato pollen project because the tomato genome annotations do not include splice variants.
Reading google group posts for rMATs has been helpful.