Build highly accurate full-length transcripts from third generation sequencing alignments.
Figure: tmerge vs StringTie2 and FLAIR for transcript-level sensitivity and precision. Measurements performed with GFFCompare on 43 SIRV datasets sequenced with ONT.
tmerge compares transcript structures (or read-to-genome alignments) present in the input and attempts to reduce transcript redundancy, i.e., merge compatible input transcripts into non-redundant transcript models.
tmerge is fast and can typically process several millions of aligned long reads in a few minutes.
pip install tmerge
It is recommended to install tmerge within a virtual environment
tmerge offers both a CLI and a Python module. The CLI is built upon the Python module and includes several built in "plugins" (see below for description of plugins).
Once you have installed tmerge via pip, the CLI will be available on your PATH. If you have installed tmerge into a virtual environment, you will need to activate that virtual environment to run the CLI.
Run tmerge --help for a description of the options.
You may import tmerge as a Python module and call the merge function to run it. merge takes 2 mandatory kwargs (input_path and output_path) and two optional kwargs (tolerance and processes). Any additional keyword arguments will be sent to any registered plugins (see below).
from tmerge import merge
input_path = "my.gff"
output_path = "output.gff"
merge(input_path=input_path, output_path=output_path) # Will block until completion
# Can now do more things with the output file
with open(output_path, "a") as f:
f.write("# A comment \n")
f.flush()Plugins allow you to "hook" into tmerge's lifecycle events and allow you to view, edit or remove the transcripts passing through tmerge and adapt it to your lab's specific needs. For example, adding Hi-Seq support.
This section explains how to write plugins and register them to tmerge.
Before writing a plugin, it is important to understand the concept of Transcript Models and Contigs. Transcripts are represented in tmerge as TranscriptModel objects, at first these are the transcripts defined in the input file but are altered throughout the lifecycle of tmerge, either having other transcript models merged into them or removed entirely. Contigs are lists of overlapping transcript models. Merging of transcript models is only performed within a contig and not between contigs.
A plugin is a simple class that registers itself to one or more "hooks" in it's init method. It receives the hooks dict as it's first argument followed by all of the kwargs that are passed to tmerge.merge.
class Counter:
def __init__(self, hooks,**kwargs):
self.count = 0
hooks["transcript_added"].tap(self.add_one)
def add_one(self, *args):
self.count += 1
def print(self, *args);
print(f"There are {self.count} transcripts")Some of the hooks send transcripts to the hooked-in function (see table below). You can edit or remove any of these transcripts and changes will be reflected in the output merged file. Further, any key/value pairs added to the meta dict will be appended to the "attributes" column of the output merged GFF.
class MyPointlessPlugin:
def __init__(self, hooks, extra_attribute, bad_id, **kwargs):
self.extra_attribute = extra_attribute
self.bad_id = bad_id
hooks["transcript_added"].tap(self.add_meta)
hooks["contig_merged"].tap(self.remove_if_matches)
def add_meta(self, transcript, *args):
# When tmerge.merge(input_path=output, output_path=output, extra_attribute="Pointless") is ran 'extra: "Pointless"' will be added to the attributes column for every transcript
self.transcript.meta["extra"] = self.extra_attribute
def remove_if_matches(self, contig, *args):
# Running tmerge.merge(input_path=output, output_path=output, bad_id="bad") will remove any transcript with the id of "bad" from the result
for transcript in contig:
if transcript.id = self.bad_id:
transcript.remove() # Flags a transcript for removalThe easiest way to provide tmerge with plugins is to pass the plugins kwarg to tmerge.merge.
from myplugins import MySimplePlugin, MyAdvancedPlugin
from tmerge import merge
merge(
input_path="input.gff",
output_path="output.gff",
plugins=[
MySimplePlugin,
MyAdvancedPlugin
]
)If you're already using setup_tools in your project, then you can use dynamic plugin discovery to easily drop in plugins to tmerge.
In your project's setup.py add your plugin to the tmerge.plugins group:
# setup.py
setup(
...
entry_points={
"tmerge.plugins": "plugin_name = my.plugin.module.MyPlugin"
}
)This will automatically register your plugin with tmerge and the plugin will be executed with tmerge.merge.
| Hook Name | When? | Arguments sent to hooked-in functions |
|---|---|---|
| chromosome_parsed | When one chromosome is parsed from the input | chromosome (list of TranscriptModels) |
| transcript_added | When a transcript is added to a contig | transcript (TranscriptModel) |
| contig_built | When one contig (group of overlapping transcripts) is built | contig (list of TranscriptModels) |
| transcripts_merged | When one transcript is merged into another | host_transcript (TranscriptModel), merged_transcript (TranscriptModel) host_transcript is the transcript that has had merged_transcript merged into it |
| contig_merged | When one contig is fully merged | contig (list of TranscriptModels) |
| contig_complete | Contig has been fully merged, transcript flagged for removal removed, and queued for writing | contig (list of TranscriptModels) |
| merging_complete | All transcripts have been merged | None |
| pre_sort | Just before the merged output is sorted | None |
| post_sort | Just after the merged output is sorted | None |
| complete | Everything complete | None |
See the tmerge/plugins/ folder for examples of various plugins.
Julien Lagarde, CRG, Barcelona, contact [email protected]
Jacob Windsor, CRG, Barcelona, contact [email protected]