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Journal Article•DOI•

Sequencing technologies-the next generation

Michael L. Metzker1•
Baylor College of Medicine1
01 Jan 2010-Nature Reviews Genetics (Nature Publishing Group)-Vol. 11, Iss: 1, pp 31-46
TL;DR: A technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments is presented.
Abstract: Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.

Summary (1 min read)

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Summary

  • DNA sequencing is one of the most important platforms for study in biological systems today.
  • The high-throughput-next generation sequencing technologies delivers fast, inexpensive, and accurate genome information.
  • Next generation sequencing can produce over 100 times more data than methods based on Sanger Sequencing.
  • The next generation sequencing technologies offered from Illumina / Solexa, ABI/SOLiD, 454/Roche, and Helicos has provided unprecedented opportunity for high-throughput functional genomic research.
  • Next generation sequence technologies offer novel and rapid ways for genome-wide characterization and profiling of mRNA's, transcription factor regions, and DNA patterns.

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ABSTRACT
Conclusion and Future Work
Next Generation Sequencing
CONTACT INFO
Data Analysis Comparisons
Downstream Analysis
REFERENCES
DNA sequencing is one of the most important platforms for
study in biological systems today. The high-throughput-next
generation sequencing technologies delivers fast,
inexpensive, and accurate genome information. Next
generation sequencing can produce over 100 times more data
than methods based on Sanger Sequencing. The next
generation sequencing technologies offered from Illumina /
Solexa, ABI/SOLiD, 454/Roche, and Helicos has provided
unprecedented opportunity for high–throughput functional
genomic research. Next generation sequence technologies
offer novel and rapid ways for genome-wide characterization
and profiling of mRNA’s, transcription factor regions, and DNA
patterns.
Fig. 7) This is a plot of the frequency of each percentage covered for all nodes.
BLAST is in blue, MUMmer is in green.
Sequencing Technologies – the Next Generation,
Micahel L. Metzkerh
Next Generation Sequencing Pipeline Development and Data Analysis
Fig. 9) This is a plot of the coverage of each Node. BLAST points are blue,
MUMmer points are red.
Fig. 6) This is a plot of the frequency of each percentage covered for all contigs.
BLAST is in blue, MUMmer is in green.
•  454/Roche – 454 Life Sciences is a Biotechnology company
that is a part of Roche and based in Branford, Connecticut.
The center develops ultra-fast high-throughput DNA
sequencing methods and tools.
•  Illumina/Solexa– Illumina is a company that develops and
manufactures integrated systems for the analysis of gene
variation. Solexa was founded to develop genome
sequencing technology.
•  ABI/SOLiD - (Sequencing by Oligonucleotide Ligation and
Detection) is a next-generation DNA sequencing technology
developed by Life Technologies and has been commercially
available since 2006. This next generation technology
generates hundreds of millions to billions of small sequence
reads at one time.
•  Helicos - Helicos's technology images the extension of
individual DNA molecules using a defined primer and
individual fluorescently labeled nucleotides, which contain a
"virtual terminator" preventing incorporation of multiple
nucleotides per cycle.
Julian Pierre
1
, Jordan Taylor
2
, Amit Upadhyay
3
, Bhanu Rekepalli
3
Fig. 8) This is a plot of the coverage of each Contig. BLAST points are blue,
MUMmer points are red.
Using the coverage of
each individual contig
ID, the results for both
BLAST and MUMmer
were plotted. While
BLAST hit more contigs,
there are more contigs
with a higher coverage
that were hit by
MUMmer.
Using the data gathered
from both BLAST and
MUMmer, the frequency
of the amount covered
for each contig was
plotted. From Fig 6), it
can be inferred that
MUMmer hit more
accurately for contigs.
Fig 4) from main.g2.bx.psu.edu
Once the results were found using both the BLAST and
MUMmer search tools, we created a program to see which
sequencing tool had the most hits per contig. The total
number of contigs in the database file is 160,749 and the
total number of nodes in the query file is 552,305. BLAST
returned a total of 123,070 hits and MUMmer returned a
total of 121,829 hits. From the results, MUMmer hit more
accurately than BLAST while BLAST hit more contigs than
MUMmer.
In Next-Generation Sequencing, data analysis is one of the
most expensive processes. While the cost of genome
sequencing goes down, the cost of analyzing data is still
expensive. In the future, the “$1,000 genome will come with
a $20,000 analysis price tag.”
The same process was
done with the Nodes.
From Fig 7), it can be
inferred that BLAST hit
more accurately with
nodes. However, there
are more BLAST results
with lower coverage.
The future of next generation sequencing can be broken
down into a variety of categories such as personalized
medicine, bio fuels, climate change, and other life science
fields.
•  Personalized Medicine is a medical model that proposes
the customization of medical decision to tailor an
individual
•  Bio Fuels present a source of alternative energy.
Microalgal biofuels use algae to synthesize the fuel. In
order to optimize the process, an understanding of the
gene-function relationship of algae would prove helpful.
•  Climate change is the active study of past and future
theoretical models which uses the past climate data to
make future projections.
In conclusion, we hope to contribute the knowledge we
have gained to contribute to fields such as these.
The same process was
done with the Nodes.
While BLAST hit more
Nodes, there are more
Nodes that hit with a
lower coverage using
BLAST.
1 Texas Southern University, 2 Austin Peay State University, 3 University of Tennessee
Next Gen Sequencing uses a wide array of tools to obtain results based
on the genome sequence. The most widely used Tools are BLAST,
HMMER, and MUMmer.
•  BLAST (Basic Local Alignment Search Tool) is a multi-sequence
alignment tool developed by NIH (National Institute of Health). It is
used find similar regions in different sequences and then compare
their similarities.
•  MUMmer (Maximum Unique Matches) is a rapid alignment system
used for rapidly aligning entire genomes. It can also align incomplete
genomes and can easily handle 1000’s of contigs from a shotgun
sequencing project.
•  HMMER (Hidden Markov Modeler) is used for searching sequence
databases for homologs of protein sequences, and for making protein
sequence alignments. It implements methods using probabilistic
models called profile hidden Markov models (HMMs)
Genome Assembly
Sequence Analysis refers to
the process of subjecting a
DNA, RNA or peptide
sequence to a wide range of
analytical methods to:
•  Compare sequences to find
similarities and infer if they
are Homologous
•  To identify the features of
the sequence such as gene
structure, distribution,
introns and exons, and
regulation of gene
expression
•  Identify Sequence
differences and variations
such as mutations
Fig. 1) This is figure shows three different Next Generation Sequencing methods. [2]
Fig. 2) Taken from A Hitchhiker’s Guide to Next-Generation Sequencing, by Gabe Rudy
Fig. 3) Taken from bio.davidson.edu/courses. Shows alignment results for yeast.
Fig 5) from jcvi.org shows the mapping of chr6 of a Human Genome
Julian Pierre – julz_pierre@yahoo.com
Jordan Taylor – jtaylor74@my.apsu.edu
Amit Upadhyay – aupadhy1@utk.edu
Bhanu Rekepalli – brekapal@utk.edu
•  http://www.roche.com/research_and_development/r_d_overview/
r_d_sites.htm?id=18
•  http://www.pnas.org/content/99/6/3712/F1.expansion.html
•  http://www.yerkes.emory.edu/nhp_genomics_core/Services/
Sequencing.html
•  http://www.illumina.com/technology/solexa_technology.ilmn
•  http://blast.ncbi.nlm.nih.gov/Blast.cgi
•  https://main.g2.bx.psu.edu/u/dan/p/fastq
•  http://ori.dhhs.gov/education/products/n_illinois_u/datamanagement/
datopic.htmll
•  http://www.jcvi.org/medicago/include/images/chr6.BamHI.maps.jpg
•  Gabe Rudy, (2010) A Hitchhiker’s Guide to Next-Generation
Sequencing, :1-9, Golden Helix
•  [1] John D. McPherson, (2009) Next-Generation Gap, 6:1-4, Nature
Methods Supplement
•  [2]Michael L. Metzker, (2010) Sequencing Technologies, - the next
generation, 11:1-5, Nature Reviews
•  Md. Fakruddin,Khanjada Shahnewaj Bin mannan, (2012) Next
Generation sequencing technologies – Principles and prospects,
6:1-9, Research and Reviews in Biosciences
•  Misra N., Panda P. K., Parida B. K., Mishra B. K., (2012)
Phylogenomic Study of Lipid Genes Involved in Mocroalgal Biofuel
Production – Candidate Gene Mining and Metabolic Pathway
Analyses, Evolutionary Bioinformatics 8:545-564, doi: 10.4137/
EBO.S10159
Galaxy is an open, web-based
platform for data intensive
biomedical research. It can be
used on its own free public
server where you can perform,
reproduce, and share complete
analyses.
An example of how Galaxy
reflects its data is shown in Fig 5.
Two FASTA files related to the same nucleotide sequence
were input into both BLAST and MUMmer and the results
were parsed into tables. Then, the coverage of all hit contigs
and nodes from both programs was found.
Citations
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Patent•
Methods of monitoring conditions by sequence analysis

[...]

Malek Faham, Thomas D. Willis
09 Nov 2009
TL;DR: In this article, the authors used DNA sequencing to identify personalized biomarkers in patients with autoimmune disease and other conditions, which can be used to determine the disease state for a subject with an autoimmune disease or other condition.
Abstract: There is a need for improved methods for determining the diagnosis and prognosis of patients with conditions, including autoimmune disease and cancer. Provided herein are methods for using DNA sequencing to identify personalized biomarkers in patients with autoimmune disease and other conditions. Identified biomarkers can be used to determine the disease state for a subject with an autoimmune disease or other condition.

119 citations

Journal Article•DOI•
Lung microbiome for clinicians. New discoveries about bugs in healthy and diseased lungs.

[...]

Leopoldo N. Segal1, William N. Rom, Michael D. Weiden•
New York University1
24 Jan 2014-Annals of the American Thoracic Society
TL;DR: Quantitative and/or qualitative changes in the lung microbiome may be relevant for disease progression and exacerbations in a number of pulmonary diseases and future investigations with longitudinal follow-up to understand the dynamics of the lungs microbiome may lead to the development of new therapeutic targets.
Abstract: Microbes are readily cultured from epithelial surfaces of the skin, mouth, and colon. In the last 10 years, culture-independent DNA-based techniques demonstrated that much more complex microbial communities reside on most epithelial surfaces; this includes the lower airways, where bacterial culture had failed to reliably demonstrate resident bacteria. Exposure to a diverse bacterial environment is important for adequate immunological development. The most common microbes found in the lower airways are also found in the upper airways. Increasing abundance of oral characteristic taxa is associated with increased inflammatory cells and exhaled nitric oxide, suggesting that the airway microbiome induces an immunological response in the lung. Furthermore, rhinovirus infection leads to outgrowth of Haemophilus in patients with chronic obstructive pulmonary disease, and human immunodeficiency virus–infected subjects have more Tropheryma whipplei in the lower airway, suggesting a bidirectional interaction in which the host immune defenses also influence the microbial niche. Quantitative and/or qualitative changes in the lung microbiome may be relevant for disease progression and exacerbations in a number of pulmonary diseases. Future investigations with longitudinal follow-up to understand the dynamics of the lung microbiome may lead to the development of new therapeutic targets.

118 citations

Journal Article•DOI•
These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure

[...]

Qingpeng Zhang1, Jason Pell1, Rosangela Canino-Koning1, Adina Howe1, C. Titus Brown1 •
Michigan State University1
25 Jul 2014-PLOS ONE
TL;DR: The speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-MER counts for individual k-mers are analyzed.
Abstract: K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We present the khmer software package for fast and memory efficient online counting of k-mers in sequencing data sets. Unlike previous methods based on data structures such as hash tables, suffix arrays, and trie structures, khmer relies entirely on a simple probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits online updating and retrieval of k-mer counts in memory which is necessary to support online k-mer analysis algorithms. On sparse data sets this data structure is considerably more memory efficient than any exact data structure. In exchange, the use of a Count-Min Sketch introduces a systematic overcount for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we analyze the speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-mer counts for individual k-mers. We also compare the performance of khmer to several other k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC, Turtle and KAnalyze. Finally, we examine the effectiveness of profiling sequencing error, k-mer abundance trimming, and digital normalization of reads in the context of high khmer false positive rates. khmer is implemented in C++ wrapped in a Python interface, offers a tested and robust API, and is freely available under the BSD license at github.com/ged-lab/khmer.

118 citations

Journal Article•DOI•
Comparing a few SNP calling algorithms using low-coverage sequencing data.

[...]

Xiaoqing Yu1, Shuying Sun1, Shuying Sun2•
Case Western Reserve University1, Texas State University2
17 Sep 2013-BMC Bioinformatics
TL;DR: The results show that the agreement between different calling algorithms is relatively low, and more caution should be used in choosing algorithms, setting filtering parameters, and designing validation studies, and for reliable SNV calling results.
Abstract: Many Single Nucleotide Polymorphism (SNP) calling programs have been developed to identify Single Nucleotide Variations (SNVs) in next-generation sequencing (NGS) data. However, low sequencing coverage presents challenges to accurate SNV identification, especially in single-sample data. Moreover, commonly used SNP calling programs usually include several metrics in their output files for each potential SNP. These metrics are highly correlated in complex patterns, making it extremely difficult to select SNPs for further experimental validations. To explore solutions to the above challenges, we compare the performance of four SNP calling algorithm, SOAPsnp, Atlas-SNP2, SAMtools, and GATK, in a low-coverage single-sample sequencing dataset. Without any post-output filtering, SOAPsnp calls more SNVs than the other programs since it has fewer internal filtering criteria. Atlas-SNP2 has stringent internal filtering criteria; thus it reports the least number of SNVs. The numbers of SNVs called by GATK and SAMtools fall between SOAPsnp and Atlas-SNP2. Moreover, we explore the values of key metrics related to SNVs’ quality in each algorithm and use them as post-output filtering criteria to filter out low quality SNVs. Under different coverage cutoff values, we compare four algorithms and calculate the empirical positive calling rate and sensitivity. Our results show that: 1) the overall agreement of the four calling algorithms is low, especially in non-dbSNPs; 2) the agreement of the four algorithms is similar when using different coverage cutoffs, except that the non-dbSNPs agreement level tends to increase slightly with increasing coverage; 3) SOAPsnp, SAMtools, and GATK have a higher empirical calling rate for dbSNPs compared to non-dbSNPs; and 4) overall, GATK and Atlas-SNP2 have a relatively higher positive calling rate and sensitivity, but GATK calls more SNVs. Our results show that the agreement between different calling algorithms is relatively low. Thus, more caution should be used in choosing algorithms, setting filtering parameters, and designing validation studies. For reliable SNV calling results, we recommend that users employ more than one algorithm and use metrics related to calling quality and coverage as filtering criteria.

118 citations

Cites background from "Sequencing technologies-the next ge..."

  • ..., template amplification, florescent intensity detection, and base calling) are involved in NGS technologies [17]....

    [...]

Journal Article•DOI•
Whole-exome sequencing of human pancreatic cancers and characterization of genomic instability caused by MLH1 haploinsufficiency and complete deficiency

[...]

Linghua Wang1, Shuichi Tsutsumi1, Tokuichi Kawaguchi2, Koichi Nagasaki2, Kenji Tatsuno1, Shogo Yamamoto1, Fei Sang1, Kohtaro Sonoda3, Kohtaro Sonoda1, Minoru Sugawara2, Akio Saiura2, Seiko Hirono4, Hiroki Yamaue2, Hiroki Yamaue5, Yoshio Miki2, Minoru Isomura2, Yasushi Totoki3, Genta Nagae1, Takayuki Isagawa1, Hiroki R. Ueda1, Satsuki Murayama-Hosokawa1, Tatsuhiro Shibata3, Hiromi Sakamoto3, Yae Kanai3, Atsushi Kaneda1, Tetsuo Noda2, Hiroyuki Aburatani1 •
University of Tokyo1, Japanese Foundation for Cancer Research2, National Cancer Research Institute3, Wakayama Medical University4, Tokyo Medical and Dental University5
01 Feb 2012-Genome Research
TL;DR: The data suggest that MLH1 hemizygous deletion, through increasing the rate of indel mutations, could drive the development and progression of sporadic cancers.
Abstract: Whole-exome sequencing (Exome-seq) has been successfully applied in several recent studies. We here sequenced the exomes of 15 pancreatic tumor cell lines and their matched normal samples. We captured 162,073 exons of 16,954 genes and sequenced the targeted regions to a mean coverage of 56-fold. This study identified a total of 1517 somatic mutations and validated 934 mutations by transcriptome sequencing. We detected recurrent mutations in 56 genes. Among them, 41 have not been described. The mutation rates varied widely among cell lines. The diversity of the mutation rates was significantly correlated with the distinct MLH1 copy-number status. Exome-seq revealed intensive genomic instability in a cell line with MLH1 homozygous deletion, indicated by a dramatically elevated rate of somatic substitutions, small insertions/deletions (indels), as well as indels in microsatellites. Notably, we found that MLH1 expression was decreased by nearly half in cell lines with an allelic loss of MLH1. While these cell lines were negative in conventional microsatellite instability assay, they showed a 10.5-fold increase in the rate of somatic indels, e.g., truncating indels in TP53 and TGFBR2, indicating MLH1 haploinsufficiency in the correction of DNA indel errors. We further analyzed the exomes of 15 renal cell carcinomas and confirmed MLH1 haploinsufficiency. We observed a much higher rate of indel mutations in the affected cases and identified recurrent truncating indels in several cancer genes such as VHL, PBRM1, and JARID1C. Together, our data suggest that MLH1 hemizygous deletion, through increasing the rate of indel mutations, could drive the development and progression of sporadic cancers.

117 citations

Cites background from "Sequencing technologies-the next ge..."

  • ...This approach is substantially cost- and labor-efficient (Schuster 2008; Metzker 2010; Biesecker et al. 2011)....

    [...]

  • ...However, because Sanger sequencing is performed on single amplicons, its throughput is limited, and large-scale sequencing projects are expensive and laborious (Schuster 2008; Metzker 2010)....

    [...]

  • ...The advent of next-generation sequencing (NGS) technologies has brought a high level of efficiency to genome sequencing (Schuster 2008; Metzker 2010)....

    [...]

References
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RNA-Seq: a revolutionary tool for transcriptomics

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Zhong Wang1, Mark Gerstein1, Michael Snyder1•
Yale University1
01 Jan 2009-Nature Reviews Genetics
TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
Abstract: RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.

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"Sequencing technologies-the next ge..." refers background in this paper

  • ...For example, in gene-expression studies microarrays are now being replaced by seq-based methods , which can identify and quantify rare transcripts without prior knowledge of a particular gene and can provide information regarding alternative splicing and sequence variation in identified gene...

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Journal Article•DOI•
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Daniel R. Zerbino1, Ewan Birney1•
European Bioinformatics Institute1
01 May 2008-Genome Research
TL;DR: Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies and is in close agreement with simulated results without read-pair information.
Abstract: We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.

9,389 citations

Journal Article•DOI•
Genome sequencing in microfabricated high-density picolitre reactors

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Marcel Margulies, Michael Egholm, William E. Altman, Said Attiya, Joel S. Bader, Lisa A. Bemben, Jan Berka, Michael S. Braverman, Yi-Ju Chen, Zhoutao Chen, Scott Dewell, Lei Du, J. M. Fierro, Xavier V. Gomes, Brian C. Godwin, Wen He, Scott Edward Helgesen, Chun Heen Ho, Gerard P. Irzyk, Szilveszter C. Jando, Maria L. I. Alenquer, Thomas P. Jarvie, Kshama B. Jirage, Jong-Bum Kim, James R. Knight, Janna R. Lanza, John H. Leamon, Steven Lefkowitz, Ming Lei, Jing Li, Kenton Lohman, Hong Lu, Vinod Makhijani, Keith Mcdade, Michael P. McKenna, Eugene W. Myers1, Elizabeth Nickerson, John Nobile, Ramona Plant, Bernard P. Puc, Michael T. Ronan, George T. Roth, Gary J. Sarkis, Jan Fredrik Simons, John Simpson, Maithreyan Srinivasan, Karrie R. Tartaro, Alexander Tomasz2, Kari A. Vogt, Greg A. Volkmer, Shally H. Wang, Yong Wang, Michael P. Weiner3, Pengguang Yu, Richard F. Begley, Jonathan M. Rothberg •
University of California, Berkeley1, Rockefeller University2, Rothberg Institute For Childhood Diseases3
15 Sep 2005-Nature
TL;DR: A scalable, highly parallel sequencing system with raw throughput significantly greater than that of state-of-the-art capillary electrophoresis instruments with 96% coverage at 99.96% accuracy in one run of the machine is described.
Abstract: The proliferation of large-scale DNA-sequencing projects in recent years has driven a search for alternative methods to reduce time and cost. Here we describe a scalable, highly parallel sequencing system with raw throughput significantly greater than that of state-of-the-art capillary electrophoresis instruments. The apparatus uses a novel fibre-optic slide of individual wells and is able to sequence 25 million bases, at 99% or better accuracy, in one four-hour run. To achieve an approximately 100-fold increase in throughput over current Sanger sequencing technology, we have developed an emulsion method for DNA amplification and an instrument for sequencing by synthesis using a pyrosequencing protocol optimized for solid support and picolitre-scale volumes. Here we show the utility, throughput, accuracy and robustness of this system by shotgun sequencing and de novo assembly of the Mycoplasma genitalium genome with 96% coverage at 99.96% accuracy in one run of the machine.

8,434 citations

Journal Article•DOI•
Origins and Mechanisms of miRNAs and siRNAs

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Richard W. Carthew1, Erik J. Sontheimer1•
Northwestern University1
20 Feb 2009-Cell
TL;DR: This work has revealed unexpected diversity in their biogenesis pathways and the regulatory mechanisms that they access, which has direct implications for fundamental biology as well as disease etiology and treatment.

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Journal Article•DOI•
Evolution and functions of long noncoding RNAs

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Chris P. Ponting1, Peter L. Oliver1, Wolf Reik2•
University of Oxford1, University of Cambridge2
20 Feb 2009-Cell
TL;DR: The evolution of long noncoding RNAs and their roles in transcriptional regulation, epigenetic gene regulation, and disease are reviewed.

4,277 citations

"Sequencing technologies-the next ge..." refers background in this paper

  • ...and to elucidate the role of non-coding RNAs in health and diseas...

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