Nov 19, 2008

Metagenomics of the effects of antibiotics on the human gut


Dethlefsen L, Huse S, Sogin ML, Relman DA
PLoS Biology Vol. 6, No. 11, e280 doi:10.1371/journal.pbio.0060280

A paper in PLOS Biology from the Relman lab investigates the effect of a treatment with the antibiotic ciprofloxacin on the bacteria in the intestine. They collected over 7,000 full-length 16S rDNA sequences (1100-1400 bp) by Sanger sequencing and over 900,000 reads (~250 bp) from 454 sequencing of the V3 and the V6 regions. 

There are many important results in this paper, but it is particularly relevant that 454 sequencing reveals more taxonomic variation with greater stability than traditional sequencing. In my own work, I have found that sequence variants that occur only once in the experiment cannot be used to differentiate samples. Deep sequencing reveals more taxa, and also reduces the frequency of singletons. A rare sequence variant (OTU) that occurs only once in the ~7000 full-length sequences occurs about 65 times in the 454 data set, providing more than enough "probability of detection" to be used for comparisons between samples. 


"This set of 7,208 sequences is among the largest datasets of full-length 16S rRNA sequences from the human microbiota (or any environment), the rarefaction curves for V6 and V3 tag pyrosequencing eventually rise higher and display more curvature toward the horizontal than the OTU0.01 curve. These features show that a single run of the [454] FLX sequencer targeting V6 or V3 tags from the human gut microbiota can reveal more taxa, and capture a larger proportion of the detectable taxa, than a more extensive effort directed toward full-length 16S rRNA clone sequencing."

journal-pbio-0060280-g003





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Nov 12, 2008

CisGenome new software for Chip-Seq

CisGenome - just published in Nov. Nature Biotechnology.
An integrated software system for analyzing ChIP-chip and ChIP-seq data.
Ji H, Jiang H, Ma W, Johnson DS, Myers RM, Wong WH.
Nat Biotechnol. 2008 Nov;26(11):1293-300.

A full-function integrated bioinformatics suite for ChIP-chip and ChIP-Seq including peak-finding, FDR control for single samples, subtraction of control lane, visualization and annotation of peaks on known genomes, and Motif finding.  Functional GUI on Windows and Mac. Wow. 

Software website here:  CisGenome
http://www.biostat.jhsph.edu/~hji/cisgenome/index.htm

Abstract:
We present CisGenome, a software system for analyzing genome-wide chromatin immunoprecipitation (ChIP) data. CisGenome
is designed to meet all basic needs of ChIP data analyses, including visualization, data normalization, peak detection, false
discovery rate computation, gene-peak association, and sequence and motif analysis. In addition to implementing previously
published ChIP–microarray (ChIP-chip) analysis methods, the software contains statistical methods designed specifically
for ChlP sequencing (ChIP-seq) data obtained by coupling ChIP with massively parallel sequencing. The modular design of
CisGenome enables it to support interactive analyses through a graphic user interface as well as customized batch-mode
computation for advanced data mining. A built-in browser allows visualization of array images, signals, gene structure,
conservation, and DNA sequence and motif information. We demonstrate the use of these tools by a comparative analysis of
ChIP-chip and ChIP-seq data for the transcription factor NRSF/REST, a study of ChIP-seq analysis with or without a negative
control sample, and an analysis of a new motif in Nanog- and Sox2-binding regions.
Posted by at 14 comments:

Oct 28, 2008

Gene-Boosted Assembly

Steven Salzberg describes a method for de novo assembly of a bacterial genome (Pseudomonas aeruginosa strain PAb1 = 6.2 MB) from a set of 33 bp Solexa fragments, using two closely related strains as reference sequences, and "boosting" assembly using predicted protein coding regions.

Salzberg SL, Sommer DD, Puiu D, Lee VT (2008) Gene-Boosted Assembly of a Novel Bacterial Genome from Very Short Reads. PLoS Comput Biol 4(9): e1000186. doi:10.1371/journal.pcbi.1000186

The AMOS assembler used in this project employs several different software modules and a considerable amount of hands-on effort. 

AMOScmp is a comparative alignment tool - it aligns short reads to a similar reference genome, and then builds contigs. This avoids the challenge of all-vs-all assembly for de novo genome sequencing projects. 

Minimus is a highly stringent assembler that uses Smith-Waterman alignments to identify overlaps between reads.

Contigs were then scanned for protein coding sequences using a combination of Glimmer and BLAST. The ABBA program uses protein coding information - especially at the ends of contings and singletons to close gaps.

Velvet was also used to independently assemble all the reads into contigs, them MUMMer was used to combine contigs and fill gaps. 

==================

This method is not going to work for every de novo sequencing problem, but we are going to try something similar for some new Plasmodium and Trichomonas species. 

All software from the Salzberg lab at the Univ. of Maryland is freely available here:

and a page describing the Short Read Assembly methods here:




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Oct 20, 2008

Public Chip-Seq Data

Here are some Chip-Seq data sets that have been published and are out there in the public domain.



NHLBI

Valouev et al, Sidow lab @ Stanford, 

Robertson et al, 2007, Nature Methods  4(8) 651-7.
Eland processed sequence reads and FindPeaks output for Stat1 and FoxA2 transcription factors






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File Formats

What is it with bioinformatics people and file formats?!

Why is it so bloody hard to produce and agree on a single standard to represent sequence data (with quality scores) and a standard for sequence reads aligned on a reference genome? With so many formats, we are all spending exponential amounts of time writing converters between all possible combinations. 

Here are some of the file formats that I've dealt with in the past couple of weeks:

SEQUENCE FORMATS

Sequence plus Phred quality score encoded as single ascii bytes

@NCYC361-11a03.q1k bases 1 to 1576
GCGTGCCCGAAAAAATGCTTTTGGAGCCGCGCGTGAAAT
+NCYC361-11a03.q1k bases 1 to 1576
!)))))****(((***%%((((*(((+,**(((+**+,-


Solexa/Illumina FASTQ like thing...
s_*_sequence.txt
@HWI-EAS305_3-30gf5aaxx:8:1:415:1852
GTTAGATTTTGTGTAACTTGCATGTAATGTTAAAA
+HWI-EAS305_3-30gf5aaxx:8:1:415:1852
YYYYYYYYYYYYVYYYYYYVYYYYYYYYVYVVTUU
@HWI-EAS305_3-30gf5aaxx:8:1:187:1286
GTTACACTGAAAAACAAATTCGTTGGAAACGGGAT
+HWI-EAS305_3-30gf5aaxx:8:1:187:1286
YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYTVVVV
@HWI-EAS305_3-30gf5aaxx:8:1:202:440
GTGAAAAATGAGAAATGCACACTGAAGGACCTGGA
+HWI-EAS305_3-30gf5aaxx:8:1:202:440
YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYVVUVV

s_*_eland_extended.txt
Solexa output format from Eland extended
>HWI-EAS305_3-30gf5aaxx:8:1:63:487      GGAGGTAGAGGTATATGGCAAGAAAACTGAAAATC     NM      -
>HWI-EAS305_3-30gf5aaxx:8:1:415:1852    GTTAGATTTTGTGTAACTTGCATGTAATGTTAAAA     3:1:0   chr14.fa:35121238F35,35121282F35,35121326F32T1T,351
21354F4T30
>HWI-EAS305_3-30gf5aaxx:8:1:187:1286    GTTACACTGAAAAACAAATTCGTTGGAAACGGGAT     0:4:5   chr6.fa:103599157R16C17A,chr2.fa:98502709R16C18,985
02829R6A9C18,98505080F4AC29,98505200F1A14C18,98505320F16C18,98506416R16C13C2CA,98506537R16C18,chrX.fa:139917587R16C2A13CA
>HWI-EAS305_3-30gf5aaxx:8:1:202:440     GTGAAAAATGAGAAATGCACACTGAAGGACCTGGA     3:87:58 chr2.fa:98503100F33T1,98506780F35,98507265F35
>HWI-EAS305_3-30gf5aaxx:8:1:359:505     TATTCAATTTACATACTCTGGCTTTGCCAACATTT     1:0:0   chr9.fa:31339651R35
>HWI-EAS305_3-30gf5aaxx:8:1:1290:135    TTGATTGTATAGTAGGGGTGAAATGGAATTTTATC     1:0:1   chrM.fa:14790R35
>HWI-EAS305_3-30gf5aaxx:8:1:627:596     GTGATTTTGAAAGTTGTAGATTGTGTGTTTGTGAT     NM      -
>HWI-EAS305_3-30gf5aaxx:8:1:379:298     GACGTGAAATATGGCGAGGAAAACTGAAAAAGGTG     31:56:28        -

s_*_eland_multi.txt
Solexa output format from Eland extended
>HWI-EAS305_3-30gf5aaxx:8:1:414:208     GTAAACTATCAATAAAATAATTTGTTACTCTGTAT     20:7:0
>HWI-EAS305_3-30gf5aaxx:8:1:59:857      TAAATTGTCCACCTTTTTCAGTTTTCCTCGCTATA     0:0:35
>HWI-EAS305_3-30gf5aaxx:8:1:1414:307    GAGAAAACTGTAAATAAAGGTAAATGAGAAAAAAA     NM
>HWI-EAS305_3-30gf5aaxx:8:1:330:1758    GGTAAAGTCCACTAAGGAAAAGAAAGAAACAATGT     1:0:0   chr7.fa:97764095R0
>HWI-EAS305_3-30gf5aaxx:8:1:576:127     GAAGTCAATCTTATGAGTTATTAGGATGGCTACTC     0:7:255 chr7.fa:111867683F1,chr12.fa:51788781R1,115833262F1
,chr6.fa:21403822R1,89734675R1,89780759R1,chrX.fa:15525553R1
>HWI-EAS305_3-30gf5aaxx:8:1:88:1045     GTTTCTCATTTTCCATGATTTTCAGTTTTCTTGCC     66:110:72
>HWI-EAS305_3-30gf5aaxx:8:1:939:613     TACTTTACTTTCTAGGGAATGTTCACTTCTAAGTG     1:0:0   chr1.fa:150051845R0

s_*_sorted.txt
filtered eland_extended alignments w/ quality  scores and genome positions
HWI-EAS305      3-30gf5aaxx     8       66      580     1584                    AGTATGGGTATCGGTTGGTGCAGAGAACTACTGCA     YYYYYYYYYYYYYYYYYYY
YYYYYVYYYYYVVUVU        chr10.fa                3001045 F       35      11
HWI-EAS305      3-30gf5aaxx     8       100     534     1062                    ATTTTCAGGTTGGAGTGACTCGCTAAAACAGCCAA     YYYYYYYYYYYYYYYYYYY
YYYYYYYYYYYTVVVV        chr10.fa                3002892 R       35      29
HWI-EAS305      3-30gf5aaxx     8       59      199     495                     CCACATGCTGTGGCAAAGCCCTTCTGAGCGGGGCG     YYYYTYYYYYYYYYYYRYY
YYYYYYYYYYYTVUVV        chr10.fa                3008958 F       34A     20
HWI-EAS305      3-30gf5aaxx     8       76      779     1406                    AGATGTACAAATGCTCCTCAGATGTTTGTGTCATA     YYYYYYYYYYYYYYYYYYY
YYYYYYYYYYYVVVVV        chr10.fa                3009290 F       35      3
HWI-EAS305      3-30gf5aaxx     8       83      547     1480                    ATCCAAACAGTTACACAAAGTTTTGAGAACATTAT     YYYYYYYYYYYYYYYYYYY
YYYYYYYYYYYVVVVV



GENOME ALIGNMENT FORMATS

SGA ('Simplified' Genome Annotation)

GFF  (General Feature Format)
EXAMPLE:
track name=regulatory description="TeleGene(tm) Regulatory Regions"
chr22 TeleGene enhancer 1000000 1001000 500 + . touch1
chr22 TeleGene promoter 1010000 1010100 900 + . touch1
chr22 TeleGene promoter 1020000 1020000 800 - . touch2

FPS (Functional Position Set)
Native format for Eukaryotic Promoter Database

EXAMPLE:
FP   Pv snRNA U1         :+S  EM:J03563.1          1+       352; 17001.098
FP   Ath snRNA U2.5      :+S  EM:AL353994.1        1-     73709; 24016.116
FP   Ath snRNA U5        :+S  EM:X13012.1          1+       678; 23040.
FP   Ta histone H3       :+S  EM:X00937.1          1+       186; 07001.

WIG (Wiggle)
UCSC Genome Browser track format

EXAMPLE
track type=wiggle_0 name="Bed Format" description="BED format" \
visibility=full color=200,100,0 altColor=0,100,200 priority=20
chr19 59302000 59302300 -1.0
chr19 59302300 59302600 -0.75
chr19 59302600 59302900 -0.50

UCSC Genome Browser
Example:
Here's an example of an annotation track that uses a complete BED definition:

track name=pairedReads description="Clone Paired Reads" useScore=1
chr22 1000 5000 cloneA 960 + 1000 5000 0 2 567,488, 0,3512
chr22 2000 6000 cloneB 900 - 2000 6000 0 2 433,399, 0,3601

ALN
Alignment format for CisGenome

chr1[tab]359077[tab]F
chr1[tab]376890[tab]R

….

column1 = chromosome where the read is aligned;
column2 = coordinate where the read is aligned;
column3 = ‘F’ or ‘+’: if the read is aligned to the forward strand of the genome assembly;
         ‘R’ or ‘-’: if the read is aligned to the reverse complement strand of the genome.
Posted by at 3 comments:

Oct 19, 2008

Service Providers

Next-Gen Sequencing as a service (you don't need half a million $$ to play this game)

454, ABI SOLID, many related services, located in Beverly MA (USA)

434, ABI SOLID, located in Houston, TexasLink

Illumina GA II, can buy a single lane w/ multiplex primers, located in St. Louis MO
Article in "In Sequence"

Illumina Genome Analyzer, located in Geneva Switzerland

Illumina, 454, and ABI, located in Germany

Illumina Genome Analyzer, custom bioinformatics
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Applications

This is where the real action is. New applications for Chip-Seq technology are developing with the unlimited creativity of the worldwide scientific community.

Genome Resequencing

SNP discovery/detection

Chip-Seq (transcription factor studies, epigenetics)

RNA-Seq (transcriptome, digital gene expresion)

MetaGenomics
The New Science of Metagenomics (National Academies Press - free PDF)

Read this FREE online!
Full Book | PDF Summary | PDF Report Brief | Podcast


Mapping translocation breakpoints


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Commercial Software

A large number of vendors are developing or adapting products to server the Next-Gen Sequencing market.  I will try to collect as much info as possible here with very brief description of functions. We can add comments or review pages for each. 


A LIMS and analysis system for complete lab management workflow

A LIMS system that tracks sample submission, automates analysis pipeline commands, and keeps track of the resulting data.

both desktop and server based NG Genomics tools, assembly, chip-seq, transcriptome,

solutions for transcript mapping (digital gene expression)  and Chip-seq with an emphasis on mapping sequence tags to annotated genome regions (TF binding sites).

A full service bioinformatics pipeline and consultant as an online service - if you have a next-gen machine and no bioinformatics support!!

DNA*
desktop solution for assembly of NG data.

from Softgenetics
de novo assembly, SNP detection, and transcriptome/digital gene expression


Posted by at 3 comments:

Conferences


Upcoming conferences (and links to content from recent confs. if I can find any)

Cambridge Healthtech Inst: Next Generation Sequencing
meets every 6 months, next one in March '09, San Diego CA


Nov 3-7 Caltech, San Diego, CA


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MagazineS & Articles

These are news articles, editorials, etc about Nex-Gen Sequencing. 
Published stuff that are not official refereed journal articles.


"The Inside Road on Genome Sequencing"
a GenomeWeb publication



Nature: Big Data special 

Nature Genetics


HHMI Bulletin

Venture Beat

BioIT World




Posted by at 1 comment:

Chip-Seq

I have been working quite hard on Chip-Seq applications for Illumina (Solexa) data.


These boil down to four basic functions:
  • peak calling - taking sequence reads aligned to a reference genome and counting the number of hits per genome interval, subtracting background or a control lane, smoothing, cutting off shoulders, splitting double peaks, and coming up with some statistic that suggests that the peaks are real vs. false positives
  • annotation - finding the location of peaks on the genome as compared to known features, especially the transcription start sites of known genes
  • visualization - looking at peaks in one of the genome browsers 
  • motif detection - finding patterns of common bases within the peaks, comparing these patterns with known transcription factor binding sites

We have evaluated quite a few different pieces of software that supply various of these functions:

"An integrated software system for analyzing ChIP-chip and ChIP-seq data"
Ji H, Jiang H, Ma W, Johnson DS, Myers RM, Wong WH.


FindPeaks
BC Cancer Agency: FindPeaks
This is a good peak finder, easy to use, with a reasonable statistical model (based on comparison of your genome mapped data vs. a MonteCarlo random distribution of tags)

SISSRS (Site Identification from Short Sequence Reads)
Makes use of +/- strand information in Chip-Seq reads to precisely identify transcription factor binding sites within a few tens of base pairs. 


written by Yong Zhang and Tao Liu from the lab of Shirley Liu at Harvard

C++ program (requires C++ compiler) - author Anton Valouev in Sidow lab at Stanford
Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data, Valouev, et al. Nature Methods 5, 829 - 834 (2008)



Peak finder and visualization via UCSB Genome Browser

MIT Integrative Genome Viewer
note the alignment processor that creates tag counts from Next-Gen aligned reads (such as Eland output files)

Web-based peak calling at the Swiss Institute of Bioinformatics


ChIPDiff - identification of differential histone modification sites by comparison of two ChIP-Seq libraries prepared from different tissues (various cell types, stages, or environmental responses). Uses a Hidden Markov Model to identify differences in ChIP tag counts.
http://bioinformatics.oxfordjournals.org/cgi/content/full/24/20/2344
Available from Genome Institue of Singapore


Posted by at 4 comments:

Software

Software for basic next-gen sequencing operations.
Each of the commercial vendors has their own proprietary software, so we will emphasize the open source.

A great page about Next-Gen software on SeqAnswers

Image Processing


Basecalling

alternative base calling for 454 sequencer with improved quality scores. Developed by the Marth lab at Boston College

Open source primary data analysis for next-gen DNA sequencers


Alignment to a Reference Sequence

Maq   Mapping and Assembly with Quality

Gapped alignments to reference genome, another from the Marth lab at Boston College

Novoalign from Novocraft in Kuala Lumpur, Malaysia

SOAP  —  Short Oligonucleotide Alignment Program
GNU Public License
from the Bioinformatics Dept of the Beijing Genomics Institute

Maps sequence reads to genomic location, variable number of mismatches and overall quality cutoff score. By Andrew D. Smith and Zhenyu Xuan in the Zhang lab at Cold Spring Harbor.

ZOOM (Zillions Of Oligos Mapped)
Product of the Michael Zhang lab at Cold Spring Harbor
"Zoom is freely available to non-commercial users at http://bioinfor.com/zoom"


de novo Assembly

Edena  (Exact de novo Assembler)
De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer.
D. Hernandez, P. François, L. Farinelli, M. Osteras, and J. Schrenzel.

Velvet  (GPL, 64 bit Linux)
Velvet: algorithms for de novo short read assembly using de Bruijn graphs.
D.R. Zerbino and E. Birney. Genome Research 18:821-829.

SHARCGS (SHort read Assembler based on Robust Contig extension for Genome Sequencing)
Dohm JC, Lottaz C, Borodina T, Himmelbauer H. SHARCGS, a fast and highly accurate short-read assembly 
algorithm for de novo genomic sequencing. 

ALLPATHS

ALLPATHS: De novo assembly of whole-genome shotgun microreads
Jonathan Butler, Iain MacCallum, Michael Kleber, Ilya A. Shlyakhter, Matthew K. Belmonte, Eric S. Lander, Chad Nusbaum, and David B. Jaffe
Genome Res. 2008 18: 810-820.

SSAKE  (GNU Public License, written in Perl)
The Short Sequence Assembly by K-mer search and 3' read Extension (SSAKE) is a genomics application for aggressively assembling millions of short nucleotide sequences by progressively searching for perfect 3'-most k-mers using a DNA prefix tree.
René L Warren, Granger G Sutton, Steven JM Jones, Robert A Holt. 2007.
Assembling millions of short DNA sequences using SSAKE. 
Bioinformatics. 23:500-501.

SNP Discovery
Another from the Marth Lab


Genome Viewers

Another tool from the Marth Lab

from Affymetrix & GenoViz
some IGB tips from Hunstman Cancer Inst. @ Univ of Utah




LIMS




Posted by at 2 comments:

Nex-Gen Blogs

These are some of the good folks posting blogs full of useful Next-Gen sequencing information:

INDEPENDENT BLOGS:

SEQanswers
"The next-generation sequencing community"


Anthony Fejes is a gradstudent in bioinformatics at UBC in Vancouver.
He works on FindPeaks and the Vancouver Short Read Analysis Package.

MassGenomics
"Medical Genomics in the post-genome era"

Genetic Future: Daniel MacArthur
The genetic and evolutionary basis of human variation, 
and the companies trying to sell you information about your genome.

Systems Biology & Bioinformatics

Jonathan Eisen writes about a lot more than Next-Gen Sequencing, but his blog is a must-read for everyone in bioinformatics, genomics, and evolutionary biology


VENDOR BLOGS:

(developer of CLC Genomics Workbench)

Geospiza


DISCUSSION LIST:

Posted by at 1 comment:

Next Gen Sequencing Vendors

A list of the vendors of next-generation, high-throughput DNA sequencing machines. 
No editorializing here, there will be comment pages for each of these. 

The Big Three:

454 (Roche)

Illumina (Solexa)

ABI SOLID


The New New Thing:

Helicos

Pacific Biosciences
[plans to ship first unit in 2010]

The Polonator (Dover Systems aka George Church & Co.)

Intelligent Biosystems

VisiGen Biotechnologies






Posted by at 3 comments:
Greetings ultra-sequencers and bioinformatics geeks.

The community of next-generation sequencing and its supporting bioinformatics is developing very rapidly, but also fragmenting into many factions. It has become very difficult for anyone to keep track of what is going on in the many different technologies, software development projects, and the various commentators.

I am going to try to make this blog a catchall where people can keep up on news from the technology vendors, survey progress in all sequencing related software, and keep up to date on relevant journal publications.

I can't do it all myself, so everyone is welcome to send in notes on relevant material as soon as they notice it.  Contributors may be invited to become co-authors. 

—Cheers,
Stuart Brown
NYU Bioinformatics Core
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