1. Effective Parallel Multicore-optimized K-mers Counting Algorithm
notes notes
Tomáš Farkaš, Peter Kubán, Mária Lucká
Harrachov, Czech Republic
Slovak University of Technology
Faculty of Informatics and Information Technologies

2. Content Bioinformatics & k-mers K-mers in bioinformatics
K-mers counting methods/algorithms/software
Algorithm preview
Preprocessing
Sorting
Counting
Experiments and results
Next (ongoing) work
at first
shortly
briefly

3. Bioinformatics & k-mers
{A, C, G , T}
adenine (A), cytosine (C), guanine (G), thymine (T)
reads, sequences, ...
k-mer: substring of length k
counting k-mers
k-mers frequencies
“trivial task”
ATGGAAATGGAATAATC
GAATCACGTAAACTTCG
GGGGGTAAACGTTCTTA
TTGGAAGTCGCGGAATC
AATCATGGAAGGTTCTT
CCGGAAGTCGTTAAACG
ATGGAAGTCGCGGAATC
Palpanas

4. Bioinformatics & k-mers17
{A, C, G , T}
adenine (A), cytosine (C), guanine (G), thymine (T)
reads, sequences, ...
k-mer: substring of length k
counting k-mers
k-mers frequencies
“trivial task”
ATGGAAATGGAAAGGTC

5. Bioinformatics & k-mers17
{A, C, G , T}
adenine (A), cytosine (C), guanine (G), thymine (T)
reads, sequences, ...
k-mer: substring of length k
counting k-mers
k-mers frequencies
“trivial task”
ATGGAAATGGAAAGGTC
ATGGAAA
  TGGAAAT
    GGAAATG
       GAAATGG
          AAATGGA
            AATGGAA
              ATGGAAA
               TGGAAAG
                    GGAAAGG
                      GAAAGGT
                         AAAGGTC 11
7-mers
k-mers in read = read length – k + 1

6. Bioinformatics & k-mers17
{A, C, G , T}
adenine (A), cytosine (C), guanine (G), thymine (T)
reads, sequences, ...
k-mer: substring of length k
counting k-mers
k-mers frequencies
“trivial task” => but 4k
ATGGAAATGGAAAGGTC
ATGGAAA
  TGGAAAT
    GGAAATG
       GAAATGG
          AAATGGA
            AATGGAA
              ATGGAAA
               TGGAAAG
                    GGAAAGG
                      GAAAGGT
                         AAAGGTC 2x 1x
Palpanas (in his presentation "Big Sequence Management")
4 to the power of k
k-mers in read = read length – k + 1

7. K-mers in bioinformatics
de novo assembly
error correction (reads, sequences)
repeat sequences detection
finding mutations
multiple sequence alignment
...
de novo assembly - De Bruijn graph

8. K-mers counting methods/algorithms/software
different methods
memory-based, disk-based, ...
using bloom filter, sorting, ...
Tallymer (2008)
Jellyfish (2011)
BFCounter (2011)
DSK (2012)
MSPKmerCounter (2013)
KMC (2013), KMC 2 (2014)
KAnalyze (2014)
Khmer (2014)
Turtle (2014)
Our algorithm(2015)
...

9. Algorithm preview Parallelism (OpenMPI, POSIX) Preprocessing Sorting
k-mers generation
partitions identification
Sorting
k-mers distribution
Nested Bucket sort algorithm
Counting
using combination of OpenMPI and POSIX threads
our algorithm can be spit into 3 stages or phases

10. Preprocessing AAAAAACCCAAACGGC
k-mers generation
reads => k-mers
encoding k-mers
{A = 00, C = 01, G = 10, T = 10}
64-bit number (k <= 32)
partitions identification
N highest bits
cumulative count
k-mers distribution*
AAAAAACCCAAACGGC

11. Preprocessing AAAAAACCCAAACGGC AAAAACCTCAAAGGGC AAAAAGTGCACACCAC . .
AACGAAGGCCCTTACA AACGAAGGCCCTTACA AACGAAGGCCGGGTTT .
TTTTAAACGCCTAACTA
TTTTAACCGTCTTCTATT TTTTATCGCTATTCTACT
k-mers generation
reads => k-mers
encoding k-mers
{A = 00, C = 01, G = 10, T = 10}
64-bit number (k <= 32)
partitions identification
N highest bits
cumulative count
k-mers distribution*

12. Sorting masters, workers k-mers distribution*
Bucket sort => nested Bucket sort

13. Sorting masters, workers k-mers distribution*
Bucket sort => nested Bucket sort

14. Sorting
100M elements
1st pass - for counting the elements in groups to be created, cumulative counts form group starting indices.
2nd pass - represents time needed for actual shifting the elements into groups, sorting indicates the time required for sorting the newly created groups.
Farkaš T.: Parallel Bucket sort algorithm for ordering short DNA sequences.
In IIT.SRC 2015: Student research conference. 11th Students research conference in informatics and information technologies Bratislava,
pp ISBN (2015)

15. Counting
sorted k-mers

16. Experiments and results
Drosophila melanogaster (common fruit fly)
Illumina Genome Analyzer II (SRX040485)
genome size: MB
reads: # (30-fold), length 76
Dataset
FASTQ size (GB)
# reads
# unique k-mers
# distinct k-mers
total # k-mers
subset 1
2,1
subset 2
4,1
subset 3
6,1
subset 4
8,1
whole data
9,8

17. Experiments and results
how to compare?
1 node
limited by available memory
# reads \ time[s]
Jellyfish
KMC 2
BFCounter
Our algorithm
17,1
7,4
257
8,1
32,4
14,3
443
12,4
48,9
21,4
688
17,2
54,5
28,9
887
22,3
61,2
36,8
980
26,5

18. Experiments and results

19. Experiments and results
IBM iDataPlex dx360 M3
52 computing nodes connected on the high-speed network: 2x10Gb/s Ethernet (RoCE)
CPU 2x 6 cores Intel Xeon X GHz
operating memory of 48GB (24GB per processor/socket, NUMA architecture) per node.
disks are 1x 2TB 7200rpm SATA per node.
Scientific Linux 6.4 (kernel el6)
OpenMPI (version )

20. Next (ongoing) work more nodes not limited by available memory
improve partitioning
test on larger datasets
... available to download

21. Tomáš Farkaš, Peter Kubán, Mária Lucká
Thank you Questions?
Effective Parallel Multicore-optimized K-mers Counting Algorithm
Tomáš Farkaš, Peter Kubán, Mária Lucká