Version: 1.1.1
NOTE TElocal relies on specially curated and indexed GTF files, which are not packaged with this software due to their size. Please go to our website for instructions to download the prebuilt curated indices.
TElocal takes RNA-seq (and similar data) and annotates reads to both genes & transposable elements.
Created by Ying Jin, Eric Paniagua, Oliver Tam & Molly Gale Hammell, February 2014
Copyright (C) 2014-2023 Talitha Forcier, Ying Jin, Eric Paniagua, Oliver Tam & Molly Gale Hammell
Contact: mghcompbio@gmail.com
Python: 2.6.x or 2.7.x or 3.x
pysam: 0.9.x or greater
Download compressed tarball.
Unpack tarball.
Navigate into unpacked directory.
Run the following:
$ python setup.py install
If you want to install locally (e.g. /local/home/usr), run this command instead:
$ python setup.py install --prefix /local/home/usr
NOTE In the above example, you must add
/local/home/usr/bin
to the PATH variable, and
/local/home/usr/lib/pythonX.Y/site-packages
to the PYTHONPATH variable, where X refers to the major python version, and Y refers to the minor python version. (e.g. python2.7 if using python version 2.7.x, and python3.6 if using python version 3.6.x)
Many High Performance Compunting clusters (HPCs) have access to singularity which allows for the download and execution of containers, TElocal also has a container through docker, it can be downloaded by singularity thusly:
singularity pull telocal.sif docker://mhammelllab/telocal:latest
Execution is then through singularity as well:
singularity exec telocal.sif TElocal --sortByPos -b RNAseq.bam --GTF gene_annots.gtf --TE te_annots.locInd --project sample_sorted_test
usage: TElocal -b alignment-file
--GTF genic-annot-file
--TE TE-annot-file
[optional arguments]
Required arguments:
-b | --BAM alignment-file RNAseq alignment file (BAM preferred)
--GTF genic-annot-file GTF or ind file for gene annotations
--TE TE-annot-file locInd file for transposable element annotations
Optional arguments:
*Input/Output options*
--stranded [option] Is this a stranded library? (no, forward, or reverse).
no - Library is unstranded
forward - "Second-strand cDNA library (e.g. QIAseq stranded)
reverse - "First-strand" cDNA library (e.g. Illumina TruSeq stranded)
DEFAULT: no.
--sortByPos Input file is sorted by chromosome position.
--project [name] Prefix used for output files (e.g. project name)
DEFAULT: TElocal_out
*Analysis options*
--mode [TE counting mode]
How to count TE:
uniq (unique mappers only)
multi (distribute among all alignments).
DEFAULT: multi
-L | --fragmentLength [fragLength]
Average length of fragment used for single-end sequencing
DEFAULT: For paired-end, estimated from the input alignment file. For single-end, ignored by default.
-i | --iteration
maximum number of iterations used to optimize multi-reads assignment. DEFAULT: 100
*Other options*
-h | --help
Show help message
--verbose [number]
Set verbose level.
0: only show critical messages
1: show additional warning messages
2: show process information
3: show debug messages
DEFAULT: 2
--version
Show program's version and exit
NOTE BAM files must be either unsorted or sorted by queryname. If the BAM files are sorted by position, please use the --sortByPos option
If BAM files are unsorted, or sorted by queryname:
TElocal -b RNAseq.bam --GTF gene_annots.gtf --TE te_annots.locInd --project sample_nosort_test
If BAM files are sorted by coordinates/position:
TElocal --sortByPos -b RNAseq.bam --GTF gene_annots.gtf --TE te_annots.locInd --project sample_sorted_test
In our experience, we recommend around 20-30Gb of memory for analyzing human samples (hg19) with around 20-30 million mapped reads when running on a cluster.
TElocal can perform transposable element quantification from alignment results (e.g. BAM files) generated from a variety of programs. Given the variety of experimental systems, we could not provide an optimal alignment strategy for every approach. Therefore, we recommend that users identify the optimal parameters for their particular genome and alignment program in order to get the best results.
When optimizing the alignment parameters, we recommend taking these points into consideration:
Allowing sufficient number of multi-mappers during alignment
Most alignment programs provide only 1 alignment per read by default. We recommend reporting multiple alignments per read. We have found that reporting a maximum of 100 alignments per read provides an optimal compromise between the size of the alignment file and recovery of multi-mappers in many genome builds. However, we highly suggest that users optimize this parameter for their particular experiment, as this could significantly improve the quality of transposable element quantification.
Paired end sequencing input
For paired-end libraries, it is recommended that only alignments from properly paired reads are present in the input BAM file. I.e., each read 1 alignment should only have a single read 2 alignment. For example, if read 1 matched 3 genomic locations (A, B, C), then if read 2 also match 3 genomic locations (A', B', C'), then all three pairs of alignments could be used (and should be in the BAM file). However, if alignment C of read 1 was matched with more than one alignment of read 2 (e.g. C' and C*), then alignment C should be discarded (as there are unmatched alignments between read 1 and read 2). STAR only outputs properly paired alignments by default, while Bowtie2 requires the --no-mixed parameter to be used.
Specific recommendations when using STAR
STAR utilizes two parameters for optimal identification of multi-mappers --outFilterMultimapNmax and --outAnchorMultimapNmax.
The author of STAR recommends that --winAnchorMultimapNmax should be set at twice the value used in --outFilterMultimapNmax,
but no less than 50. In our study, we used the same number for both parameters (100), and found negligible differences in identifying
multi-mappers. Upon further discussion with the author of STAR, we recommend that setting the same value for --winAnchorMultimapNmax
and --outFilterMultimapNmax, though we highly suggest users test multiple values of --winAnchorMultimapNmax to identify the
optimal value for their experiment.
TElocal is part of TEToolkit suite.
TElocal is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with TElocal. If not, see this website.