Releases: ablab/IsoQuant
IsoQuant 3.7.0
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Optimized grouped counts output. By default, all counts are stored in linear format, which saves time and disk space.
Matrices with small number of columns are automatically converted to usual matrix in TSV format,
larger matrices typical for single-cell and spatial data are converted to MTX format.
See--counts_format
paramter for options. It is also possible to convert counts after IsoQuant is finished usingsrc/convert_grouped_counts.py
.
Fixes issues mentioned in #248 -
Renamed counts related to discovered transcripts and genes to avoid confusion.
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New options
--indexing_options
and--mapping_options
that allow to pass options to the indexing and mapping commands.
Fixes #284 and #259 -
STARlong is now an alternative options for aligning, can be set via
--aligner starlong
(not recommended for ONT reads).
Fixes #284 -
Exon/splice junction counts now only come from reads assigned to the same strand, fixes #253
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Use only gene-assigned reads for exon counting, fixes #283
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Fixed rare serialization bug #304
IsoQuant 3.6.3
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Fix penalty score for terminal exon elongation when selecting similar isoforms for inconsistent reads #270, thanks to @biosalt-cc
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Fix
transcript_model_grouped_counts
output format #275, thanks to @ljwharbers
IsoQuant 3.6.2
Important bug-fix release!
Fixes linear grouped counts output #258, big thanks to @qsonehara!
IsoQuant 3.6.1
IsoQuant 3.6.0
Fixes #236 by resolving duplicated noninformative
and intergenic
reads assignments.
As a results, also fixes duplicated novel transcripts. Thanks @jamestwebber for the report!
IsoQuant 3.5.2
IsoQuant 3.5.1
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Fix YAML support in visualization #222
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Fix transcript naming when IsoQuant-generated GTF is provided as input #219
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Fix
exons
attribute duplication #219 -
Exon ids are now consistent between output and input annotations if present
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New
--count_format
option for setting desired grouped counts format (matrix/linear/both), fixes #223
IsoQuant 3.5.0
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New visualization software developed by @jackfreeman88. See more here.
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Dramatically reduced RAM consumption for grouped counts, about 10-20x decrease on datasets with large number of groups.
Important fix for single-cell data processing. Should fix #189. -
Fixed #195: output GTF contained very similar isoforms and estimated their expression as 0.
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New documentation is now available at ablab.github.io/IsoQuant.
IsoQuant 3.4.2
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Dramatically reduce RAM consumption. Should fix #209.
IsoQuant 3.4.2 was tested on a simulated ONT dataset with 30M reads using 12 threads. In the default mode RAM consumption decreased from 280GB to 12GB when using the reference annotation and from 230GB down to 6GB in the reference-free mode. Running time in the default mode increased by approximately 20-25%. When using
--high_memory
option, running time remains the same as in 3.4.1, RAM consumption in the reference-based mode is 46GB, and 36GB in the reference-free mode. Note, that in general RAM consumption depends on the particular data being used and the number of threads.In brief, in 3.4.0 and 3.4.1 inadequate RAM consumption was caused by this commit. Apparently, adding a couple of
int
fields to theBasicReadAssignment
class made the default pickle serialization not to clean used memory (possibly, a leak). Since some large lists ofBasicReadAssignment
were sent between processes, this caused the main process to consume unnecessary RAM. When later new processes were created for GTF construction, total RAM consumption exploded thanks to the way Python multiprocessing works. This release implements two ways fixing the issue: sending objects via disk (default) and using custom pickle serialization (when--high_memory
is used). -
Transcript and exon ids are now identical between runs, including ones with different number of threads.