Systematic evaluation of spliced alignment programs for RNA-seq data

Systematic evaluation
of spliced alignment programs
for RNA-seq data
Engström et al. (Nature Methods 2013)
Presented by Monica Drăgan

2Functional Genomics, SS2014Dienstag, 25. März 2014
for RNA-seq data

for RNA-seq data

for RNA-seq data
© bioinformatics.ca
Mapping the reads to
●
a reference genome
or
●
a transcriptome database
Deep sequencing (with NGS)

for RNA-seq data
© bioinformatics.ca
Why RNA sequencing?
●
Functional studies
●
Gene prediction is difficult

for RNA-seq data

for RNA-seq data
Mapping strategies depend on read length
●
Read length < 50 bp
●
Read length > 50 bp

for RNA-seq data
●
Read length < 50 bp → Short (Unspliced) aligners
●
Read length > 50 bp
BWA BOWTIE

for RNA-seq data
●
Read length < 50 bp → Short (Unspliced) aligners
●
Read length > 50 bp → Spliced alignment programs
●
In mRNA sequences the introns were removed
BWA BOWTIE
GSNAP
MapSplice
STAR
PAL Mapper
TopHat
ReadsMapPASS
SMALT

Outline
 Challenges in RNA sequence alignment
 The aim of this paper
 Existing spliced-alignment software
 Conclusions

Outline
 Conclusions

Challenges in RNA-seq alignment
 Large #reads

 Large #reads → ~100M = computationally
expensive

 Large #reads → ~100M = computationally
expensive Compression with
Burrows-Wheeler
Transform

 Large #reads
 RNA Splicing

 Large #reads
 RNA Splicing / Alternative splicing

 Large #reads
 a single gene may code
for multiple proteins

 Large #reads
 Paired read separation issue

 Large #reads

 Large #reads
 Pseudogenes

 Large #reads
 Pseudogenes
 pseudogenes often have highly similar sequences to functional,
intron-containing genes → RNA reads can incorrectly be mapped
here
 the human genome, which contains over 14,000 pseudogenes [Pei
et al. Genome Biol 2012]

 Large #reads
 Pseudogenes
 Duplications

 Large #reads
 Pseudogenes
 Duplications
 may correspond to biased PCR amplification of particular fragments

Outline
 Conclusions

The aim of this paper
 Asses the performance of 26 RNA seq alignment
protocols –based on 11 programs on real and simulated
human and mouse transcriptomes
 Alignment protocols were evaluated on Illumina 76-
nucleotide
 paired-end RNA-seq data from:
 the human leukemia cell line K562 (1.3 × 109 reads)
 mouse brain (1.1 × 108 reads) and two simulated

Outline
 TopHat
 MapSplice
 STAR
 GSNAP

 Conclusions

unspliced
alignment
TopHat
Trapnell, Pachter, and Salzberg (2009)

unspliced
alignment
- reads that map to more than
10 locations
- reads that have more than a
few mismatches
TopHat

unspliced
alignment
assemble
islands of sequences
- reads that map to more than
10 locations
- reads that have more than a
few mismatches
TopHat

unspliced
alignment
assemble
Such an approach will identify only known
or predicted combinations of exons
TopHat

TopHat
unspliced
alignment
spliced
alignment

TopHat

TopHat
Known junction signals:
GT-AG, GC-AG, and AT-AC

TopHat
If an alignment extends into
an intron region, realign the reads
to the adjacent exons instead
Known junction signals:
GT-AG, GC-AG, and AT-AC

Outline
 Challenges in sequence alignment
 What the paper is about
 Existing software
 TopHat
 MapSplice
 STAR
 GSNAP
 Conclusions
 Future work

MapSplice
Wang et al. (2010)
 Similar to TopMap
 Reads = tags
 A tag has an ‘exonic alignment’ if it can be aligned in its
entirety to a consecutive sequence of nucleotides in G.
 T has a ‘spliced alignment’ if its alignment to G Requires
one or more gaps

MapSplice
Wang et al. (2010)
Step 1: exonic alignment

MapSplice
Wang et al. (2010)
Step 2: spliced alignment
●
the spliced alignment of tj+1
to the genomic interval between
anchors tj and tj+2
●
consider all the possible positions
of the splice site and map according
to the Hamming distace

MapSplice
Wang et al. (2010)
Step 3: merge candidate segment alignments

Outline
 TopHat
 MapSplice
 STAR
 GSNAP
 Conclusions
 Future work

STAR
Dobin et al. (2012)
Maximal Mappable Prefix (read location i) =
the longest read substring from position i
that has exact match on one
or more substrings of the ref genome
poor genomic alignment
Detect:
(a) splice junctions
(b) mismatches
(c) tails

Outline
 TopHat
 MapSplice
 STAR
 GSNAP
 Conclusions
 Future work

GSNAP
Wu and Nacu (2010)
Efficient detection of indels and splice pairs:
 For large genomes, it is more efficient to preprocess the
genome rather than the reads to create genomic
index files, which provide genomic positions for a given
prefix/suffix.
 Works with candidate regions in the ref genome. (keep
track of the read location of 12 residues that support each
candidate region)

GSNAP
Wu and Nacu (2010)

For a more powerful use of the algorithms:
 use of available gene annotations, which allow it to avoid
erroneously mapping reads to pseudogenes
 use the information about the pair sof the paired read

Outline
 Conclusions

Conclusions
 Mismatches and basewise accuracy
MapSplice, PASS and TopHat display a low tolerance for mismatches.
Consequently, a large proportion of reads with low base-call quality scores
were not mapped by these methods

Conclusions
●
GSNAP, GSTRUCT, MapSplice,PASS, SMALT and STAR allow missmatches an can also
output an incomplete alignment when they are unable to map an entire sequence

Conclusions
Reads from mouse were mapped (against the mouse reference assembly17) at a greater rate and
with fewer mismatches than those from K562 (the cancer cell line K562 accumulated a lot of
mutations with respect to the human reference assembly).

Conclusions
 Indel frequency
and accuracy
.
●
GSTRUCT produced the most uniform
distribution of indels
(coefficient of variation (CV) = 0.32)
●
TopHat produced the most variable
distribution
(CV = 1.5 and 1.1 splice junctions)
Size distribution of indels
for the human K562 data set
Precision and recall, stratified by indel size
GEM and PALMapper output included more
indels than any other method

Conclusions
 Indel frequency
and accuracy
●
GEM and PALMapper report many false indels
(precision)
●
GSNAP and GSTRUCT exhibit high sensitivity
for deletions, independent of size (recall)
●
TopHat2 protocol is the most
sensitive method for long insertions (recall)
Precision and recall, stratified by indel size

Conclusions
 Spliced alignment
●
High accuracy discovery rate for
ReadsMap, GSNAP, GSTRUCT and
MapSplice and TopHat
●
#false junction calls was greatly reduced
if junctions were filtered by supporting
alignment counts (plot c)
●
Protocols using annotation recovered
nearly all of the known junctions in
expressed transcripts (plot d)
●
For novel-junction discovery,
GSTRUCT outperformed other methods
●

Conclusions
 GSNAP, GSTRUCT, MapSplice and STAR compared
favorably to the other methods
 MapSplice seems to be a conservative aligner with respect to
mismatch frequency, indel and exon junction calls.
 The most significant issue with GSNAP, GSTRUCT and
STAR is the presence of many false exon junctions in the
output.
 Both GSNAP and GSTRUCT require considerable computing
time when parameterized for sensitive spliced alignment

Thank you!

 Remaining challenges:
 Remaining challenges include exploiting gene annotation
with-
 out introducing bias, correctly placing multimapped reads,
achiev-
 ing optimal yet fast alignment around gaps and
mismatches, and
 Analysis
 reducing the number of false exon junctions reported.
Ongoing
 developments in sequencing technology will demand
efficient
 processing of longer reads with higher error rates and will
require
 more extensive spliced alignment as reads span multiple

 Some RNA-seq aligners, including GSNAP [5], RUM [6],
and STAR [7], map reads independently of the alignments
of other reads, which may explain their lower sensitivity for
these spliced reads
 GSNAP [5] and STAR [7] also make use of annotation,
although they use it in a more limited fashion in order to
detect splice sites

 have shown how suffix arrays (Manber
 and Myers, 1990), compressed using a Burrows-Wheeler
Transform
 (BWT) (Burrows and Wheeler, 1994), can rapidly map
reads that
 are exact matches or have a few mismatches or short
insertions or
 deletions (indels) relative to the reference.


 A third approach, provided by the QPALMA program (Bona
 et al., 2008), can align individual reads across exon–exon
junctions
 using Smith–Waterman-type alignments and a specifically
trained
 splice site model.


Systematic evaluation of spliced alignment programs for RNA-seq data

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