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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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TL;DR: A protocol for rapid and inexpensive preparation of hundreds of multiplexed genomic libraries for Illumina sequencing by carrying out the Nextera tagmentation reaction in small volumes, replacing costly reagents with cheaper equivalents, and omitting unnecessary steps is presented.
Abstract: Whole-genome sequencing has become an indispensible tool of modern biology. However, the cost of sample preparation relative to the cost of sequencing remains high, especially for small genomes where the former is dominant. Here we present a protocol for rapid and inexpensive preparation of hundreds of multiplexed genomic libraries for Illumina sequencing. By carrying out the Nextera tagmentation reaction in small volumes, replacing costly reagents with cheaper equivalents, and omitting unnecessary steps, we achieve a cost of library preparation of $8 per sample, approximately 6 times cheaper than the standard Nextera XT protocol. Furthermore, our procedure takes less than 5 hours for 96 samples. Several hundred samples can then be pooled on the same HiSeq lane via custom barcodes. Our method will be useful for re-sequencing of microbial or viral genomes, including those from evolution experiments, genetic screens, and environmental samples, as well as for other sequencing applications including large amplicon, open chromosome, artificial chromosomes, and RNA sequencing.

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References
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Journal Article•DOI•
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.

11,528 citations

"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•
Velvet: Algorithms for de novo short read assembly using de Bruijn graphs

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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.

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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.

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Origins and Mechanisms of miRNAs and siRNAs

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Richard W. Carthew1, Erik J. Sontheimer1•
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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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Evolution and functions of long noncoding RNAs

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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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