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Transcript Segment Library Construction for RNA-Seq Quantification
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Yanagi: Transcript Segment Library Construction for RNA-Seq Quantification

Update July 10th, 2018: Check our recent manuscript for detailed formulation of segments and their usage in gene and alternative splicing analysis

Source code based on the work presented in paper to appear in proceedings of WABI 2017


Analysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, and that annotated transcripts from a given gene are often a small subset of many possible complete transcripts for that gene. Here we describe Yanagi, a tool which segments a transcriptome into disjoint regions to create a segments library from a complete transcriptome annotation that preserves all of its consecutive regions of a given length L while distinguishing annotated alternative splicing events in the transcriptome. In this paper, we formalize this concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens. Finally, we demonstrate the utility of quantification using a segment library based on an analysis of differential exon skipping in Drosophila melanogaster and Homo sapiens. The notion of transcript segmentation as introduced here and implemented in Yanagi will open the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of analyses of transcription variation.




Yanagi has been developed and tested in Python 3.7 and R 3.5. Yanagi uses the following modules: Python:

  • tqdm R (Bioconductor):
  • GenomicFeatures
  • Biostrings

Command and subcommand structure

Yanagi works with a command/subcommand structure: subcommand options

where the subcommand can be one of these options:

  • preprocess : Preprocesses transcriptome annotation by breaking exons into disjoint exonic bins and find their transcript mapping.
  • segment : Generates a set of maximal L-disjoint segments from the preprocessed transcriptome annotation.
  • align : Pseudo aligns reads (single or paired-end) into the segments and obtain segment counts (single segment or segment pair counts).

Annotation Preprocessing

Exons (and retained introns) in the transcriptome annotation can be overlapping within a gene (e.g. in 3'/5' splicing) or across genes. In order for Yanagi to guaranteeing L-disjointness property of the generated segments, a preprocessing step is needed to generate disjoint exonic bins. Yanagi generate disjoint exonic bins and their transcripts mappings from an input annotation file (GTF format) and the genome sequence file (FASTA format).

Command and options

To preprocess the transcriptome annotation subject to segmentation one has to run the following command in the following format:

python preprocess -gtf <gtf-file> -fa <fasta-file> -o <output-directory>

Note that throughout this tutorial, we will use the same directory as the working directory when needed in different commands.

Output files

The preprocess operation outputs two files:

  1. disjoint_bins.tsv: A file with the structural and sequence information of each constructed disjoint exonic bin.
	chr	start	end	strand	seq
1	1	11869	11871	+	GTT
2	1	11872	11873	+	AA
  1. txs2bin.tsv: A file with transcripts-to-bins information.
	chr	geneID	txID	bins	strand
1	1	ENSG00000223972	ENST00000456328	1,2,3,4,5,6,8,9,11,12,13,14,15,16,17,18	+
2	1	ENSG00000223972	ENST00000515242	2,3,4,5,6,8,9,12,13,14,15,16,17,18,19	+
3	1	ENSG00000223972	ENST00000518655	3,4,5,6,7,8,9,14,15,17,18	+

Segments Generation

This command executes the main operation preparing the segments library by Yanagi, to be used later for RNA-seq reads alignment. Yanagi takes the preprocessed transcriptome as input to build segments graph, which is then parsed to generate minimal L-disjoint segments.

Yanagi's Segments Example

Fig 1. The figure shows an illustrative example of transcriptome segmentation of one gene with three transcripts. The example shows the final segments generated by yanagi and how reads are aligned to them.

Command and options

To segment the transcriptome one has to run the following command in the following format:

python segment -l <read-length> -wd <work-directory>

List of options available:

Output files

The segmentation operation outputs three files:

  1. .fa: A FASTA file of the segments library representing the transcriptome.
  1. .meta: A file of metadata describing the structure of each segment and how it was formed.
segID	chrom	geneID	txAnnIDs	binIDs	st	end	strand
SEG0000001	10	ENSG00000012779	ENST00000542434	57010,57011	45869661	45869774	+
SEG0000002	10	ENSG00000012779	ENST00000374391,ENST00000542434	57011,57012,57013,57014,57015,57016,57017	45869675	45920450	+
SEG0000003	10	ENSG00000012779	ENST00000374391,ENST00000483623,ENST00000542434	57016,57017	45919539	45920580	+
SEG0000004	10	ENSG00000012779	ENST00000483623	57017,57018	45920481	45923934	+
  1. .gtf: A GTF file describing both exonic bins and segments. This file is intended for visualization of segments. Check Section Visualization.

This GTF contains entries of two possible feature type (column 3): exonic_bin or segment. Each exonic bin or segment has only one entry in the file. Entries that describe lists (like transcripts for exonic bins, or bins for segments) are placed as lists separated by '+'.

  • Exonic_bin feature examples:
1	hg19_segs101	exonic_bin	11869	11871	.	+	.	gene_id "ENSG00000223972"; entry_id "1"; transcripts "ENST00000456328";
1	hg19_segs101	exonic_bin	11872	11873	.	+	.	gene_id "ENSG00000223972"; entry_id "2"; transcripts "ENST00000456328+ENST00000515242";
1	hg19_segs101	exonic_bin	11874	12009	.	+	.	gene_id "ENSG00000223972"; entry_id "3"; transcripts "ENST00000456328+ENST00000515242+ENST00000518655";
1	hg19_segs101	exonic_bin	12010	12057	.	+	.	gene_id "ENSG00000223972"; entry_id "4"; transcripts "ENST00000450305+ENST00000456328+ENST00000515242+ENST00000518655";
  • Segment feature examples:
10	hg19_segs101	segment	45869661	45869774	.	+	.	gene_id "ENSG00000012779"; entry_id "SEG0000001"; exonic_bin_ids "57010+57011"; transcripts "ENST00000542434";
10	hg19_segs101	segment	45869675	45920450	.	+	.	gene_id "ENSG00000012779"; entry_id "SEG0000002"; exonic_bin_ids "57011+57012+57013+57014+57015+57016+57017"; transcripts "ENST00000374391+ENST00000542434";
10	hg19_segs101	segment	45919539	45920580	.	+	.	gene_id "ENSG00000012779"; entry_id "SEG0000003"; exonic_bin_ids "57016+57017"; transcripts "ENST00000374391+ENST00000483623+ENST00000542434";
10	hg19_segs101	segment	45920481	45923934	.	+	.	gene_id "ENSG00000012779"; entry_id "SEG0000004"; exonic_bin_ids "57017+57018"; transcripts "ENST00000483623";

P.S. Section Ready-to-Download Segments Libraries provides pre-prepared segment libraries of some commonly used genomes.

Alignment To Segments

This command runs a given alignment command to align RNA-seq reads into the segments library as a reference. The main use of this command is to facilitate aligning paired-end reads to obtain segment-pair counts.

In this tutorial, we assume to use RapMap ( to perform pseudo-alignment. However, other alignment tools can be used and the following commands are to be adjusted accordingly. Note! The indexing step is done separately from yanagi's pipeline. As an example, to index the segments library using RapMap, follow this command format:

PATH/TO/RAPMAP quasiindex -t PATH/TO/segments.fa -i quasiindex/output/directory

Once the aligner's index is ready, one can run the alignment step using Yanagi's following command:

python align -ref <segments-meta> -o <output-filename> -cmd1 'PATH/TO/RAPMAP quasimap -i quasiindex/output/directory -r PATH/TO/FIRST_READS.fa' -cmd2 'PATH/TO/RAPMAP quasimap -i quasiindex/output/directory -r PATH/TO/SECOND_READS.fa'

Alternatively, use the provided shell script by first editing the variables used in it, and choosing the right command for single or paired-end modes.

List of options available:

  • -ref | --segs-ref-file: Specifies the segments reference metadata file (.fa.meta file generated by segment subcommand) to align reads against. Note that its corresponding FASTA file has to be indexed by the used aligner a priori.

  • -o | --output-name: This is a name of the output segment count file.

  • -cmd1| --align-command1: This is the command used to run Rapmap's quasi mapping for the reads FASTA file <PATH/TO/FIRST_READS.fa> using segments indexed at <quasiindex/output/directory>.

  • -cmd2| --align-command2: (Optional) For the second-end reads (if paired-end reads).

Output files

The segmentation operation outputs three files:

.txt: A text (TSV file) contains segments counts (or segment-pairs counts if paired-end).

  • Segments counts output example (Single-end reads):
segID	count	geneID	segLen	segStLoc
SEG0232653	7	ENSG00000000457	510	169822815
SEG0232655	11	ENSG00000000457	1039	169824007
SEG0232667	3	ENSG00000000460	1105	169631245
SEG0232671	12	ENSG00000000460	166	169764190
  • Segment-Pair counts output example (Paired-end reads):
seg1ID	seg2ID	count	geneID	seg1Len	seg2Len	seg1StLoc	seg2StLoc	txs
SEG0232653	SEG0232653	4	ENSG00000000457	510	510	169822815	169822815	ENST00000367772
SEG0232653	SEG0232655	1	ENSG00000000457	510	1039	169822815	169824007	ENST00000367772
SEG0232655	SEG0232653	2	ENSG00000000457	1039	510	169824007	169822815	ENST00000367772
SEG0232655	SEG0232655	8	ENSG00000000457	1039	1039	169824007	169824007	ENST00000367772,ENST00000367771,ENST00000367770

Note: txs field for segment-pairs counts represent the intersecton of transcripts from both segments.


Yanagi's Visualization Example

To visualize segments of a specific gene, run the R script found in R/vizGeneSegments.R using Rstudio or the following Rscript command:

Rscript R/vizGeneSegments.R <geneID> <segments.fa.meta> <segmentsDir> <output-filename>

Support for visualizing segment counts will be added soon.

Segment-based PSI Calculation

After samples are aligned to the segments using command align, one can process the segments/segment-pairs counts obtained to perform alternative splicing analysis. Yanagi provides a command to calculat PSI values based on segments counts in each of the aligned samples. This command calculates PSI values of alternative splicing events based on their segment mappings.

Command and options

To calculate PSI values one has to run the following command in the following format:

python psiCalc  -es <events-to=segments-mapping> -s <segments-meta> -i <segment-counts-directory> -o <output-directory> -opf <output-prefix>

List of options available:

Output files

The PSI calculation operation outputs a .psi file per sample. Each .psi file is of the following format:

eventID	incCount	exCount	PSI	incSegs	exSegs	incTxs	exTxs	incSegsLen	exSegsLen	incLen
ENSG00000177697;SE:11:836442-836769:836843-837250:+	6.150537634408602	0.6363636363636364	0.9061	SEG0009700,SEG0009707	SEG0009701	ENST00000322008,ENST00000397420,ENST00000397421,ENST00000524748,ENST00000526693,ENST00000527341,ENST00000528011,ENST00000530320,ENST00000530726	ENST00000529810	372	198	74
ENSG00000177697;SE:11:833026-834530:834591-836063:+	0.6874154262516915	0.41695501730103807	0.62188	SEG0009666,SEG0009671,SEG0009673,SEG0009684,SEG0009685,SEG0009686	SEG0009667,SEG0009668,SEG0009678,SEG0009679,SEG0009680	ENST00000322008,ENST00000531999	ENST00000397421,ENST00000526661,ENST00000529810,ENST00000530155	739	578	61
ENSG00000214063;SE:11:842915-847201:847300-850288:+	0.6484149855907781	0.4393939393939394	0.59552	SEG0010382,SEG0010384,SEG0010395,SEG0010396,SEG0010397	SEG0010383	ENST00000397397	ENST00000397411	694	198	99

Ready-to-download Segment Libraries

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