Test-driving parallel compression software

Next generation sequencing is driving genomics into realm of big data, and this will only continue as NGS moves into the hands of more and more researchers and clinicians. Genome sequence datasets are huge and place an immense demand on informatics infrastructure including disk space, memory and CPU usage. Bioinformaticians need to be aware of the performance of different compression software available to make the most out of their hardware.

In this post, we'll test the performance of some compression algorithms in terms of their disk-saving ability as well as speed of decompression/compression.

The file that we're working on is an RNA-seq fastq file that is 5.0 GB in size and contains 25.8 million sequence reads on 103.0 million lines. Here is the top 10 lines.

@SRR1171523.1 Y40-ILLUMINA:15:FC:5:1:8493:1211 length=36
ACTTGCTGCTAATTAAAACCAACAATAGAACAGTGA
+SRR1171523.1 Y40-ILLUMINA:15:FC:5:1:8493:1211 length=36
B55??4024/13425>;>>CDD@DBB<<<BB<4<<9
@SRR1171523.2 Y40-ILLUMINA:15:FC:5:1:3017:1215 length=36
GAACAATCCAACGCTTGGTGAATTCTGCTTCACAAT
+SRR1171523.2 Y40-ILLUMINA:15:FC:5:1:3017:1215 length=36
DBBE?BDFDEGGGEG@GGBGDGGGGHHHHHGFHFBG
@SRR1171523.3 Y40-ILLUMINA:15:FC:5:1:3346:1218 length=36
TTCCACTTCCCAAGTAAACAGTGCAGTGAATCCTGT

The system performing the benchmark is a pretty standard desktop PC with 8 threads.
The system used to benchmark compression software
I performed compression using the default settings and determined the compression ratio. bzip2 was better than gzip, but not as good as plzip, pxzip or lrzip.
Data compression characteristics. Compressed fastq file size compared to the uncompressed file (top). Compression ratio of various compression software (bottom).
Next I determined the time to read a compressed file and output to /dev/null. Then determined the time required to compress and write to disk. These tests show that parallel gzip (pigz) is definitely the fastest way to read/compress a file on this system. plzip2 was quick for decompression but pretty slow for compression. pxz and  lrzip were slow for both decompression and compression. Their CPU and memory requirements may be useful if other jobs will be running concurrently.
Comparison of different compression algorithms with respect to time (left side), peak memory (centre) and CPU utilisation (right) of decompression (top), compression to /dev/null (middle) and compression with write to disk (bottom).

The pigz program was certainly the stand out in terms of performance and would be recommended for files that might be read regularly. Reading and writing compressed data with pigz proved to be considerably faster than using cat, probably because after compression, there is less data to write to disk. Where disk space is limiting, pbzip2 and plzip might be more applicable for data archiving.

See my post on how the benchmarking was done.

Sources:
http://unix.stackexchange.com/questions/52313/how-to-get-execution-time-of-a-script-effectively
http://www.ubuntugeek.com/how-to-clear-cached-memory-on-ubuntu.html
http://www.thegeekstuff.com/2012/01/time-command-examples/
http://ss64.com/bash/time.html
Location: St Kilda VIC 3182, Australia

Popular posts from this blog

Data analysis step 8: Pathway analysis with GSEA

Image
In our RNA-seq series so far we've performed differential analysis and generated some pretty graphs, showing thousands of differentially expressed genes after azacitidine treatment. In order to understand the biology underlying the differential gene expression profile, we need to perform pathway analysis. We use Gene Set Enrichment Analysis ( GSEA ) because it can detect pathway changes more sensitively and robustly than some methods. A 2013 paper compared a bunch of gene set analyses software with microarrays and is worth a look. Generate a rank file The rank file is a list of detected genes and a rank metric score. At the top of the list are genes with the "strongest" up-regulation, at the bottom of the list are the genes with the "strongest" down-regulation and the genes not changing are in the middle. The metric score I like to use is the sign of the fold change multiplied by the inverse of the p-value, although there may be better methods out there
Read more

Two subtle problems with over-representation analysis

Image
Over-representation analysis is a helpful and really frequently used technique for understanding trends from omics data and gene lists more generally. Just to demonstrate this, I tabulated the number of citations of some of the most popular web tools and packages, which reaches a massive 191k citations! But if you have used some of these tools, you will notice that they yield subtly different results. We were curiuous about that and ran a whole bunch of investigations into the internal workings of these tools, in particular clusterProfiler. We identified two subtle problems with clusterProfiler, which we unpack in detail in our new publication , but here I will give you a quick overview. Problem #1: The background problem The first one we call the “background problem,” because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. This results in removing a large number of unannotated genes from the b
Read more

Uploading data to GEO - which method is faster?

If you have had to upload omics data to GEO before, you'll know it's a bit of a hassle and takes a long time. There are a few methods suggested by the GEO team if you are using the Unix command line: Using 'ncftp' ncftp set passive on set so-bufsize 33554432 open  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/your @mail.com_ yourfolder put -R Folder_with_submission_files Using 'lftp' lftp  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/ your @mail.com _ yourfolder mirror -R Folder_with_submission_files Using 'sftp' (expect slower transfer speeds since this method encrypts on-the-fly) sftp  geoftp @s ftp-private.ncbi.nlm.nih.gov password: yourpasscode cd uploads/ your @mail.com _ yourfolder mkdir new_geo_submission cd new_geo_submission put file_name Using 'ncftpput' (transfers from the command-line without entering an interactive shell) Usage example: ncftpput -F -R -z -u  geoftp  -p "yourpasscode"
Read more