Systematic evaluation
of spliced alignment programs
for RNA-seq data
Engström et al. (Nature Methods 2013)
Presented by Mo...
2Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
3Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
4Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
©...
5Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
©...
6Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
7Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
M...
8Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
M...
9Functional Genomics, SS2014Dienstag, 25. März 2014
Systematic evaluation
of spliced alignment programs
for RNA-seq data
M...
10Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in RNA sequence alignment
 The aim of this pape...
11Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in RNA sequence alignment
 The aim of this pape...
12Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
13Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads → ~100M = computationa...
14Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads → ~100M = computationa...
15Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing
16Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing
17Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
18Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
19Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
20Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
21Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
22Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
23Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
24Functional Genomics, SS2014Dienstag, 25. März 2014
Challenges in RNA-seq alignment
 Large #reads
 RNA Splicing / Alter...
25Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in RNA sequence alignment
 The aim of this pape...
26Functional Genomics, SS2014Dienstag, 25. März 2014
The aim of this paper
 Asses the performance of 26 RNA seq alignment...
27Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in RNA sequence alignment
 The aim of this pape...
28Functional Genomics, SS2014Dienstag, 25. März 2014
unspliced
alignment
TopHat
Trapnell, Pachter, and Salzberg (2009)
29Functional Genomics, SS2014Dienstag, 25. März 2014
unspliced
alignment
- reads that map to more than
10 locations
- read...
30Functional Genomics, SS2014Dienstag, 25. März 2014
unspliced
alignment
assemble
islands of sequences
- reads that map to...
31Functional Genomics, SS2014Dienstag, 25. März 2014
unspliced
alignment
assemble
Such an approach will identify only know...
32Functional Genomics, SS2014Dienstag, 25. März 2014
TopHat
Trapnell, Pachter, and Salzberg (2009)
unspliced
alignment
spl...
33Functional Genomics, SS2014Dienstag, 25. März 2014
TopHat
Trapnell, Pachter, and Salzberg (2009)
34Functional Genomics, SS2014Dienstag, 25. März 2014
TopHat
Trapnell, Pachter, and Salzberg (2009)
Known junction signals:...
35Functional Genomics, SS2014Dienstag, 25. März 2014
TopHat
Trapnell, Pachter, and Salzberg (2009)
If an alignment extends...
36Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in sequence alignment
 What the paper is about
...
37Functional Genomics, SS2014Dienstag, 25. März 2014
MapSplice
Wang et al. (2010)
 Similar to TopMap
 Reads = tags
 A t...
38Functional Genomics, SS2014Dienstag, 25. März 2014
MapSplice
Wang et al. (2010)
Step 1: exonic alignment
39Functional Genomics, SS2014Dienstag, 25. März 2014
MapSplice
Wang et al. (2010)
Step 2: spliced alignment
●
the spliced ...
40Functional Genomics, SS2014Dienstag, 25. März 2014
MapSplice
Wang et al. (2010)
Step 3: merge candidate segment alignmen...
41Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in sequence alignment
 What the paper is about
...
42Functional Genomics, SS2014Dienstag, 25. März 2014
STAR
Dobin et al. (2012)
Maximal Mappable Prefix (read location i) =
...
43Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in sequence alignment
 What the paper is about
...
44Functional Genomics, SS2014Dienstag, 25. März 2014
GSNAP
Wu and Nacu (2010)
Efficient detection of indels and splice pai...
45Functional Genomics, SS2014Dienstag, 25. März 2014
GSNAP
Wu and Nacu (2010)
46Functional Genomics, SS2014Dienstag, 25. März 2014
For a more powerful use of the algorithms:
 use of available gene an...
47Functional Genomics, SS2014Dienstag, 25. März 2014
Outline
 Challenges in RNA sequence alignment
 The aim of this pape...
48Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 Mismatches and basewise accuracy
MapSplice, PASS and To...
49Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 Mismatches and basewise accuracy
●
GSNAP, GSTRUCT, MapS...
50Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 Mismatches and basewise accuracy
Reads from mouse were ...
51Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 Indel frequency
and accuracy
.
●
GSTRUCT produced the m...
52Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 Indel frequency
and accuracy
●
GEM and PALMapper report...
53Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 Spliced alignment
●
High accuracy discovery rate for
Re...
54Functional Genomics, SS2014Dienstag, 25. März 2014
Conclusions
 GSNAP, GSTRUCT, MapSplice and STAR compared
favorably t...
55Functional Genomics, SS2014Dienstag, 25. März 2014
Thank you!
56Functional Genomics, SS2014Dienstag, 25. März 2014
 Remaining challenges:
 Remaining challenges include exploiting gen...
57Functional Genomics, SS2014Dienstag, 25. März 2014
 Some RNA-seq aligners, including GSNAP [5], RUM [6],
and STAR [7], ...
58Functional Genomics, SS2014Dienstag, 25. März 2014
 have shown how suffix arrays (Manber
 and Myers, 1990), compressed...
59Functional Genomics, SS2014Dienstag, 25. März 2014
 A third approach, provided by the QPALMA program (Bona
 et al., 20...
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Systematic evaluation of spliced alignment programs for RNA-seq data

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Discussion on the paper "Systematic evaluation
of spliced alignment programs for RNA-seq data" by Engström et al. (Nature Methods 2013)

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  • /home/monique/Desktop/ETH_alignment_MDragan.odp
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • RNA Splicing Introns - mRNA transcripts do not include these introns, so the alignment program must handle gapped (or spliced) alignment with very large gaps
  • Systematic evaluation of spliced alignment programs for RNA-seq data

    1. 1. Systematic evaluation of spliced alignment programs for RNA-seq data Engström et al. (Nature Methods 2013) Presented by Monica Drăgan
    2. 2. 2Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data
    3. 3. 3Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data
    4. 4. 4Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data © bioinformatics.ca Mapping the reads to ● a reference genome or ● a transcriptome database Deep sequencing (with NGS)
    5. 5. 5Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data © bioinformatics.ca Why RNA sequencing? ● Functional studies ● Gene prediction is difficult
    6. 6. 6Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data
    7. 7. 7Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data Mapping strategies depend on read length ● Read length < 50 bp ● Read length > 50 bp
    8. 8. 8Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data Mapping strategies depend on read length ● Read length < 50 bp → Short (Unspliced) aligners ● Read length > 50 bp BWA BOWTIE
    9. 9. 9Functional Genomics, SS2014Dienstag, 25. März 2014 Systematic evaluation of spliced alignment programs for RNA-seq data Mapping strategies depend on read length ● 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
    10. 10. 10Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in RNA sequence alignment  The aim of this paper  Existing spliced-alignment software  Conclusions
    11. 11. 11Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in RNA sequence alignment  The aim of this paper  Existing spliced-alignment software  Conclusions
    12. 12. 12Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads
    13. 13. 13Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads → ~100M = computationally expensive
    14. 14. 14Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads → ~100M = computationally expensive Compression with Burrows-Wheeler Transform
    15. 15. 15Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing
    16. 16. 16Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing
    17. 17. 17Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing
    18. 18. 18Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  a single gene may code for multiple proteins
    19. 19. 19Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  Paired read separation issue
    20. 20. 20Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  Paired read separation issue
    21. 21. 21Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  Paired read separation issue  Pseudogenes
    22. 22. 22Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  Paired read separation issue  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]
    23. 23. 23Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  Paired read separation issue  Pseudogenes  Duplications
    24. 24. 24Functional Genomics, SS2014Dienstag, 25. März 2014 Challenges in RNA-seq alignment  Large #reads  RNA Splicing / Alternative splicing  Paired read separation issue  Pseudogenes  Duplications  may correspond to biased PCR amplification of particular fragments
    25. 25. 25Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in RNA sequence alignment  The aim of this paper  Existing spliced-alignment software  Conclusions
    26. 26. 26Functional Genomics, SS2014Dienstag, 25. März 2014 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
    27. 27. 27Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in RNA sequence alignment  The aim of this paper  Existing spliced-alignment software  TopHat  MapSplice  STAR  GSNAP   Conclusions
    28. 28. 28Functional Genomics, SS2014Dienstag, 25. März 2014 unspliced alignment TopHat Trapnell, Pachter, and Salzberg (2009)
    29. 29. 29Functional Genomics, SS2014Dienstag, 25. März 2014 unspliced alignment - reads that map to more than 10 locations - reads that have more than a few mismatches TopHat Trapnell, Pachter, and Salzberg (2009)
    30. 30. 30Functional Genomics, SS2014Dienstag, 25. März 2014 unspliced alignment assemble islands of sequences - reads that map to more than 10 locations - reads that have more than a few mismatches TopHat Trapnell, Pachter, and Salzberg (2009)
    31. 31. 31Functional Genomics, SS2014Dienstag, 25. März 2014 unspliced alignment assemble Such an approach will identify only known or predicted combinations of exons TopHat Trapnell, Pachter, and Salzberg (2009)
    32. 32. 32Functional Genomics, SS2014Dienstag, 25. März 2014 TopHat Trapnell, Pachter, and Salzberg (2009) unspliced alignment spliced alignment
    33. 33. 33Functional Genomics, SS2014Dienstag, 25. März 2014 TopHat Trapnell, Pachter, and Salzberg (2009)
    34. 34. 34Functional Genomics, SS2014Dienstag, 25. März 2014 TopHat Trapnell, Pachter, and Salzberg (2009) Known junction signals: GT-AG, GC-AG, and AT-AC
    35. 35. 35Functional Genomics, SS2014Dienstag, 25. März 2014 TopHat Trapnell, Pachter, and Salzberg (2009) 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
    36. 36. 36Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in sequence alignment  What the paper is about  Existing software  TopHat  MapSplice  STAR  GSNAP  Conclusions  Future work
    37. 37. 37Functional Genomics, SS2014Dienstag, 25. März 2014 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
    38. 38. 38Functional Genomics, SS2014Dienstag, 25. März 2014 MapSplice Wang et al. (2010) Step 1: exonic alignment
    39. 39. 39Functional Genomics, SS2014Dienstag, 25. März 2014 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
    40. 40. 40Functional Genomics, SS2014Dienstag, 25. März 2014 MapSplice Wang et al. (2010) Step 3: merge candidate segment alignments
    41. 41. 41Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in sequence alignment  What the paper is about  Existing software  TopHat  MapSplice  STAR  GSNAP  Conclusions  Future work
    42. 42. 42Functional Genomics, SS2014Dienstag, 25. März 2014 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
    43. 43. 43Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in sequence alignment  What the paper is about  Existing software  TopHat  MapSplice  STAR  GSNAP  Conclusions  Future work
    44. 44. 44Functional Genomics, SS2014Dienstag, 25. März 2014 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)
    45. 45. 45Functional Genomics, SS2014Dienstag, 25. März 2014 GSNAP Wu and Nacu (2010)
    46. 46. 46Functional Genomics, SS2014Dienstag, 25. März 2014 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
    47. 47. 47Functional Genomics, SS2014Dienstag, 25. März 2014 Outline  Challenges in RNA sequence alignment  The aim of this paper  Existing spliced-alignment software  Conclusions
    48. 48. 48Functional Genomics, SS2014Dienstag, 25. März 2014 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
    49. 49. 49Functional Genomics, SS2014Dienstag, 25. März 2014 Conclusions  Mismatches and basewise accuracy ● 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
    50. 50. 50Functional Genomics, SS2014Dienstag, 25. März 2014 Conclusions  Mismatches and basewise accuracy 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).
    51. 51. 51Functional Genomics, SS2014Dienstag, 25. März 2014 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
    52. 52. 52Functional Genomics, SS2014Dienstag, 25. März 2014 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
    53. 53. 53Functional Genomics, SS2014Dienstag, 25. März 2014 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 ●
    54. 54. 54Functional Genomics, SS2014Dienstag, 25. März 2014 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
    55. 55. 55Functional Genomics, SS2014Dienstag, 25. März 2014 Thank you!
    56. 56. 56Functional Genomics, SS2014Dienstag, 25. März 2014  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
    57. 57. 57Functional Genomics, SS2014Dienstag, 25. März 2014  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
    58. 58. 58Functional Genomics, SS2014Dienstag, 25. März 2014  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. 
    59. 59. 59Functional Genomics, SS2014Dienstag, 25. März 2014  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. 
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