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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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Journal Article•DOI•
HTQC: a fast quality control toolkit for Illumina sequencing data

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Xi Yang1, Di Liu1, Fei Liu1, Jun Wu1, Jing Zou1, Xue Xiao1, Fangqing Zhao1, Baoli Zhu1 •
Chinese Academy of Sciences1
31 Jan 2013-BMC Bioinformatics
TL;DR: The HTQC toolkit can generate reads quality assessment faster than existing tools, providing guidance for reads filtration utilities that allow users to choose different strategies to remove low quality reads.
Abstract: Illumina sequencing platform is widely used in genome research. Sequence reads quality assessment and control are needed for downstream analysis. However, software that provides efficient quality assessment and versatile filtration methods is still lacking. We have developed a toolkit named HTQC - abbreviation of High-Throughput Quality Control - for sequence reads quality control, which consists of six programs for reads quality assessment, reads filtration and generation of graphic reports. The HTQC toolkit can generate reads quality assessment faster than existing tools, providing guidance for reads filtration utilities that allow users to choose different strategies to remove low quality reads.

150 citations

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

  • ...Next generation sequencing technologies are generating massive sequence data [1], and different platforms can introduce varied level of sequence reads error....

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Journal Article•DOI•
A new testing strategy to identify rare variants with either risk or protective effect on disease.

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Iuliana Ionita-Laza1, Joseph D. Buxbaum2, Nan M. Laird3, Christoph Lange3, Christoph Lange4, Christoph Lange5 •
Columbia University1, Icahn School of Medicine at Mount Sinai2, Harvard University3, University of Bonn4, German Center for Neurodegenerative Diseases5
03 Feb 2011-PLOS Genetics
TL;DR: A robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits and it is shown that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small.
Abstract: Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.

150 citations

Journal Article•DOI•
Exome sequencing and complex disease: practical aspects of rare variant association studies

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Ron Do1, Sekar Kathiresan2, Sekar Kathiresan1, Gonçalo R. Abecasis3•
Harvard University1, Broad Institute2, University of Michigan3
15 Oct 2012-Human Molecular Genetics
TL;DR: Practical guidance is provided in the design and analysis of complex trait association studies focused on rare, coding variants to include rare coding variants which often have more marked functional consequences.
Abstract: Genetic association and linkage studies can provide insights into complex disease biology, guiding the development of new diagnostic and therapeutic strategies. Over the past decade, genetic association studies have largely focused on common, easy to measure genetic variants shared between many individuals. These common variants typically have subtle functional consequence and translating the resulting association signals into biological insights can be challenging. In the last few years, exome sequencing has emerged as a cost-effective strategy for extending these studies to include rare coding variants, which often have more marked functional consequences. Here, we provide practical guidance in the design and analysis of complex trait association studies focused on rare, coding variants.

150 citations

Journal Article•DOI•
Computational and statistical approaches to analyzing variants identified by exome sequencing

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Nathan O. Stitziel1, Adam Kiezun2, Adam Kiezun1, Shamil R. Sunyaev1, Shamil R. Sunyaev2 •
Brigham and Women's Hospital1, Broad Institute2
14 Sep 2011-Genome Biology
TL;DR: A large number of single nucleotide polymorphisms in the exome have been identified and many computational and statistical approaches to identify disease-association signals have emerged.
Abstract: New sequencing technology has enabled the identification of thousands of single nucleotide polymorphisms in the exome, and many computational and statistical approaches to identify disease-association signals have emerged.

150 citations

Journal Article•DOI•
The crucial role of multiomic approach in cancer research and clinically relevant outcomes

[...]

Miaolong Lu1, Xianquan Zhan•
Central South University1
21 Feb 2018-The Epma Journal
TL;DR: The methodology, advances, and clinically relevant outcomes of different “omics” technologies in cancerResearch are described, and the importance and scientific merit of integrating multi-omics in cancer research and clinical relevant outcomes are emphasized.
Abstract: Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different “omics” technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

149 citations

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

  • ...NGS benefits greatly to patients with rare cancers and cancer of unknown primary site, for detailed genomic profiling could be used to identify the main drivers of malignant transformation and to cover the shortage of diagnosis and treatment strategies [294, 295]....

    [...]

  • ...CAGE Cap analysis of gene expression CID Collision induced dissociation CML Chronic myelogenous leukemia ECD Electro capture dissociation ETD Electron transfer dissociation FTICR Fourier transform ion cyclotron resonance GC Gas chromatography HCC Hepatocellular carcinoma HP Fucosylated haptoglobin HPLC High performance liquid chromatography IARC International Agency for Research on Cancer ICGC International Cancer Genome Consortium lncRNAs Long ncRNAs LC Liquid chromatography LSCC Lung squamous cell carcinoma MALDI Matrix-assisted laser desorption ionization MDLC Multi-dimensional LC MPSS Massively parallel signature sequencing MRM Multiple reaction monitoring MS Mass spectrometry MS/MS Tandem mass spectrometry NANA N-acetylneuraminic acid ncRNAs Non-coding RNAs NGS Next-generation sequencing NMR Nuclear magnetic resonance NSCLC Non-small cell lung cancer PPPM Predictive, preventive, and personalized medicine SAGE Serial analysis of gene expression SCLC Small cell lung cancer SCO Small cell osteosarcoma * Xianquan Zhan yjzhan2011@gmail.com 1 Key Laboratory of Cancer Proteomics of ChineseMinistry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, People’s Republic of China 2 Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, People’s Republic of China 3 State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, People’s Republic of China 4 The State Key Laboratory of Medical Genetics, Central South University, 88 Xiangya Road, Changsha, Hunan 410008, People’s Republic of China SRM Selected reaction monitoring SWATH Sequential window acquisition of all theoretical mass spectra TCGA Cancer Genome Atlas TOF Time-of-flight 1DGE One-dimensional gel electrophoresis 2DGE Two-dimensional gel electrophoresis 2DDIGE Two-dimensional difference in-gel electrophoresis WGS Whole genome sequencing...

    [...]

  • ...The disadvantage that limited the application of these platforms are shorter read lengths with an average read length range from 32 to 330 bp [37]), which creates challenges for genome alignment and assemble [3, 37, 38, 40, 41]....

    [...]

  • ...999% [38], Sanger sequencing achieved a number of monumental accomplishments, including completing of the Human Genome Project [37]....

    [...]

  • ...The second-and third-generation technologies are referred to as NGS [37]....

    [...]

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Journal Article•DOI•
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Zhong Wang1, Mark Gerstein1, Michael Snyder1•
Yale University1
01 Jan 2009-Nature Reviews Genetics
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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

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Origins and Mechanisms of miRNAs and siRNAs

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Richard W. Carthew1, Erik J. Sontheimer1•
Northwestern University1
20 Feb 2009-Cell
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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.

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