1. Algorithms for Multisample Read Binning
Student: Gabriel Ilie
Major advisor: Ion Măndoiu
Associate Advisor: Sanguthevar Rajasekaran
Associate Advisor: Yufeng Wu
University of Connecticut
November 2013

2. Outline
Motivation
Previous approaches
Algorithms
Results
Ongoing work

3. Human microbiome
The microorganisms inhabiting our bodies comprise the microbiome.
Microbes outnumber our own cells by 10 to 1 [Wooley et al, 2010].
Diabetes, obesity, cancer and even attractiveness to mosquitoes seem to correlate with changes in our microbiome [Turnbaugh et al, 2010].
To better understand our own condition we also need to understand the composition of the microbial communities inhabiting our bodies and how they interact not just between themselves but also with their habitat (e.g. the host organism).

4. Single organism studies
they rely on clonal cultures
most microorganism cannot be cultivated
suffer from amplification bias
microbes do not live in single species communities
members of these communities interact not just with each other but also with their habitats, which includes the host organism
Data obtained from clonal cultures is highly biased and does not capture a true picture of microbial life.

5. Transcriptomics
Genomes only provide information about the potential function of organisms.
Having a gene does not mean that this gene is also expressed inside the host or during a particular condition.
The transcriptome is the set of all RNA molecules produced in one or a population of cells.
In order to understand the physiology of the microorganisms we need to know their transcriptome.

6. Metatranscriptomics
The transcriptome of a community is the union over the transcriptomes of each of its members.
In metatranscriptomic studies:
bulk RNA is extracted directly from environmental samples
the RNA is reverse-transcribed
the resulting RNA-Seq libraries are sequenced
Metatranscriptomic studies are essential in order to understand the physiology of the microbiome.

7. Challenges of working with metatranscriptomic data
The volume of sequencing data is several orders of magnitude larger than single organisms.
Reads can come from hundreds of different species, each with a different abundance level.
In addition to having a range in the abundance levels of the microorganisms, genes are expressed at drastically different levels.
Genes usually have multiple isoforms.
Metatranscriptomics has to deal with all of the challenges of metagenomics (1 and 2) plus some extra challenges (3 and 4), therefore algorithms devised for metagenomic data can also be applied to metatranscriptomic data.

8. (Meta)Transcriptome assembly types
Genome independent reconstruction (de novo):
de Bruijn k-mer graph
Velvet (2008)
Trinity (2011)
Genome guided reconstruction:
spliced read mapping
exon identification
splice graph
Cufflinks (2010)
TRIP (2012)

9. (Meta)Transcriptome assembly types
Genome independent reconstruction (de novo):
de Bruijn k-mer graph
Velvet (2008)
Trinity (2011)
Genome guided reconstruction:
spliced read mapping
exon identification
splice graph
Cufflinks (2010)
TRIP (2012)

10. Outline
Motivation
Previous approaches
Algorithms
Results
Ongoing work

11. Clustering reads into bins
Analysis of environmental samples is difficult.
To simplify the assembly process, many metagenomic tools have been developed to cluster the reads into bins (i.e. species).
Algorithms developed for binning metagenomic reads can also be applied to (meta)transcriptomic reads (bins represent transcripts instead of species).

12. Types of reads binning algorithms
Genome dependent
CompostBin(2008)
Metacluster(2012)
DNA composition patterns.
G+C content, dinucleotide frequencies vary amongst species.
Drawbacks:
achieve reasonable performance only for long reads (800~1000 bp, [Wu et al, 2011])
NGS technologies produce short reads
Genome independent
AbundanceBin(2011)
MultiBin(2011)
K-mer frequencies are usually linearly proportional to a genome’s abundance.
Sufficiently long k-mers are usually unique.
Works with short sequencing reads.
Drawbacks
group together reads from different species if they have close abundance levels
do not perform well on species with low abundances

13. Metacluster (2012) Two round unsupervised binning algorithm:
the first round clusters high-abundance reads
filter reads with 16-mer that appear less than T times
the reads are grouped based on shared 36-mers
the groups are clustered using 5-mer distributions
the second round clusters the remaining reads (low- abundance)
those with unique 16-mers are discarded
the reads are grouped based on shared 22-mers
the groups are clustered using 4-mer distributions
Advantages:
better binning of reads from low abundance species
uses both techniques: k-mer abundances and DNA composition patterns

14. MultiBin (2011)
Processes multiple samples (N > 1) of the same microbial community.
Clusters the reads into b bins (b is the number of species).
Binning algorithm:
all reads are pooled together
a graph G=(V, E) is generated
V is the set of reads
edges connect reads with substantial overlap (50 bp)
greedily partition the vertices into a set, the tags, s.t. each read is either a tag or affiliated with one which substantially overlaps it
cluster the set of tags using Vt=(ct1,ct2, …, ctN), cti=number of reads from sample i which substantially overlap tag t
each non-tag read is assigned to the same bin as its affiliated tag
b needs to be known in advance or estimated somehow.
Uses abundance differences from any of the samples to tell low abundance species apart.
The algorithm is quadratic in the total number of reads
Binning algorithm:
all reads are pooled together
a graph G=(V, E) is generated
V is the set of reads
edges connect reads with substantial overlap (50 bp)
greedily find a maximal independent set in G, the set of tags
each read is now either a tag or affiliated with a tag which substantially overlaps it
Vt=(ct1,ct2, …, ctN), cti=number of reads from sample i which substantially overlap tag t
perform k-medoids clustering on the set of tags
assign each non-tag read to thebin which holds its affiliated tag

15. Outline
Motivation
Previous approaches
Algorithms
Results
Ongoing work

16. Our approach
We propose a novel method for unsupervised abundance-based multiple samples reads binning algorithm
in brief, for N>1 samples:
we split the reads into k-mers and count how many times they appear in each sample
we run a sample-by-sample error removal algorithm
we pool the k-mer counts together to get N-dimensional count vectors
we run the error removal algorithm once more on all of the k-mers
we use the structure of the de Bruijn graph defined by the k-mers and the counts to partition the graph into paths or chordless cycles (putative pseudo- exons)

17. Pseudo-exons
Pseudo-exons are defined as substrings whose k-mers and (k+1)- mers appear in the same transcripts with the same multiplicity. T1 T2 T3 T4 A B C D A B C A A B C E F F E D
Exon signatures:
A: T1x1 T2x2 T3x1
B: T1x1 T2x1 T3x1
C: T1x1 T2x1 T3x1
D: T1x1 T3x1
E: T3x1 T4x1
F: T3x1 T4x1
Pseudo-exons: A BC D E F
Notice that exons E and F form different pseudo-exons because in T4 they are in reverse order compared to T3.

18. K-mer counting we count k-mers and (k+1)-mers using Jellyfish (2011)
Jellyfish was designed for shared memory parallel computers with more than one core, it uses several lock-free data structures and multi-threading to count k-mers much faster than other tools
formally, for a given value of k, we count the number of occurrences of all k-mers in each of the N samples
our algorithms assume we have strand nonspecific data, therefore the counts of complementary k-mers are summed together as they are indistinguishable
we store k-mers in canonical form (the smaller value lexicographically between a k-mer and its reverse complement)
we combine the counts over all the samples into a list of N- dimensional vectors
the maximum value for k supported by Jellyfish is 31

19. De Bruijn graph
We construct the de Bruijn graph; vertices are k-mers and edges are (k+1)-mers.
Because we store vertices in canonical form, each vertex represents two k-mers: itself and its reverse-complement.
We add an edge between any two k-mers if and only if there is a (k+1)-mer in the reads such that its prefix of length k in canonical form matches one of the vertices, while its suffix of length k in canonical form matches the other one.
We will sometimes use vertices to refer to k-mers and edges to refer to (k+1)-mers.

20. De Bruijn graph
ACG
CGT
CGC
GCG
Vertices
ACG
CGC
CGA
ATC
Edges
ACGC
CGCG
ACGA
ATCG
CGA
TCG
ATC
GAT
The lists of k-mers and (k+1)-mers define an implicit representation of the de Bruijn graph, therefore we don’t need to construct it explicitly.
Relative to the canonical form of a vertex, for each edge we can say whether it is incoming or outgoing:
if the the k-mer matches the 5’ end of either forms of an edge then that is an outgoing edge
else it is an incoming edge

21. Error removal
A common approach is to remove k-mers which have counts lower than t >= 1.
We found that even for t = 1, we lose too much information, because removing unique k-mers compromises the results for ultra- low abundance transcripts.
We found that “tip removal” and “bubble removal” give much better results [Zerbino et al, Velvet, 2008].
These methods use the structure of the de Bruijn graph instead of coverage information to remove k-mers affected by sequencing errors.

22. Tip and bubble error removal
When a read contains a sequencing error
the first few k-mers may be correct, until they start to overlap the position where the error occurred
this creates a branch going out of the “correct” path
this new branch will either end in a leaf (creating a “tip”), or if the read is long enough, the k-mers will stop overlapping the error and the branch will merge back into the path (creating a “bubble”)
A “tip” is a chain of nodes that is disconnected on one end
we expect the majority of tips to have a maximum length of 2k
removing tips is straightforward; we remove all tips which have a length up to some threshold
removing a tip does not disrupt the connectivity of the graph
Implementing “bubble” removal is still an ongoing work.

23. Partitioning the de Bruijn graph
From the de Bruijn graph we want to extract putative pseudo-exons.
These putative pseudo-exons, if we ignore self-edges, correspond to paths or chordless cycles in the de Bruijn graph.
We use the structure of the graph and the vectors with the counts to do the partitioning.

24. Partitioning the de Bruijn graph
assuming perfect data, finding the putative pseudo-exons would simply mean removing all incoming/outgoing edges out of vertices that have an in/out degree greater than 1
because we have sequencing errors we need to distinguish between erroneous and real edges
we have the following two cases
if we have a correct edge and at least one wrong edge coming out of the same vertex, then we expect the abundance of the correct edge to dwarf the sums of the erroneous, therefore the technique described earlier would remove the wrong edges and keep the correct one
if we have two correct edges coming out of the same vertex we want to remove both of them, assuming none of the edges comes from ultra-low abundance transcripts, then we expect none of the edges to pass the ratio test and our algorithm should remove both of them

25. Partitioning the de Bruijn graph
We have the following cases:
If a vertex has indegree and outdegree equal to at most 1 (it is on a path), we do nothing.
If a vertex has outdegree (indegree) greater than 1,
then we remove all outgoing (incoming) edges from that vertex
however, we keep the most abundant edge if the ratio between its abundance and the sum of the abundances of the other edges is higher than a threshold 0 < e < 1
The value of e should be close to 1 (e.g. 0.97)

26. Our approach
We propose a novel method for unsupervised abundance-based multiple samples reads binning algorithm
in brief, for N>1 samples:
we split the reads into k-mers and count how many times they appear in each sample
we run a sample-by-sample error removal algorithm
we pool the k-mer counts together to get N-dimensional count vectors
we run the error removal algorithm once more on all of the k-mers
we use the structure of the de Bruijn graph defined by the k-mers and the counts to partition the graph into paths or chordless cycles (putative pseudo- exons)

27. Outline
Motivation
Previous approaches
Algorithms
Results
Ongoing work

28. Test data - error free
GNF Atlas [Su et al, 2004] is a dataset which contains information about the expression levels of a set of genes in several human tissues
From this dataset we extracted the expression levels of 19,371 genes in 10 human tissues
We used only one isoform per gene
We simulated 30 million error free RNA-Seq paired- reads of length 50 from this dataset using a tool called Grinder (2012)

29. Test data - with sequencing errors
Grinder was very useful for simulating the error free data, however when we wanted to introduce errors its long running time became an issue.
Instead, we simulated sequencing errors by using the error free data.
We simulated only one type of errors, substitutions, because these are the most common type found in Illumina datasets.
We introduced substitutions into the error free reads with a probability of 0.1%, 0.5% and 1% per base.

30. K-mer counts in the simulated data
transcripts
reads
#30-mers
#31-mers
error k
#correct
k-mers
#incorrect
#missing k-mers
percentage of incorect k-mers
49,572,543
49,611,691 0% 30
49,512,839
59,704 31
49,546,279
65,412
0.1%
49,508,364
109,380,690
64,179
68.84%
49,540,750
108,528,925
70,941
68,66%
0.5%
49,483,820
404,415,622
88,723
89.1%
49,510,299
402,714,823
101,392
89.05% 1%
49,433,000
708,460,569
139,543
93.48%
49,446,451
706,531,643
165,240
93.46%
Because of ultra-low abundance transcripts, even in the error free data we have missing k-mers
Even for an error rate of 0.1% we notice that the number of unique k- mer more than triples when compared to the error free data, 70% of which do not appear in the transcripts
This shows the importance of error removal/correction algorithms

31. Efficiency of different error removal techniques
Error removal method
error
#correct 31-mers
#incorrect 31-mers
#missing 31-mers
none 0%
49,546,279
65,412
non-unique in at
least 1 sample
46,520,486
3,091,205
0.1%
46,257,210
6,906,069
3,354,481
remove tips <= 21 over the union of the samples
49,502,470
21,772,808
109,221
remove tips <= 60 over the union of the samples
49,236,255
12,120,069
375,436
remove tips <= 21 sample-by-sample and over the union of the samples
49,127,456
10,998,956
484,235
remove tips <= 60 sample-by-sample and over the union of the samples
48,707,567
5,304,983
904,124

32. Results for the graph partitioning
error
error removal
technique
edge removal
threshold
#pseudo-exons
>= 50bp
#pseudo-exons >= 50bp and
do not have wrong 31-mers
#30-mers
%transcriptome covered by
col 5 0%
none 1
60,285
49,299,665
99.5%
65,051
49,239,335
99.3%
0.1%
remove tips <= 60 over the union of the samples
0.97
416,853
60,335
37,815,256
76.3%
remove tips <= 60 sample-by-sample and over the union of the samples
228,841
77,010
46,699,963
94.2%
The first row (green) uses all k-mers, the counts are computed from the transcripts.

33. Outline
Motivation
Previous approaches
Algorithms
Results
Ongoing work

34. Ongoing work
We believe that bubble removal will help us get rid of most of the erroneous k-mers which are still present in the data after tip removal.
We want to incorporate an error correction algorithm, SEECER (2013), to correct the erroneous reads before doing starting the k-mer counting.
Currently our algorithms do not take into account strand strand-specific RNA-Seq data. Optimizing the algorithms to take advantage of this information, when available, represents another opportunity to improve the results of this approach.

35. Q&A