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Journal ArticleDOI

Sequencing technologies-the next generation

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 mRNAs, 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 Hitchhikers 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 ArticleDOI
TL;DR: A semi-automated, bioinformatic workflow system is constructed and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS, demonstrating its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm.
Abstract: Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism.

65 citations

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TL;DR: A probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework is developed and can be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.
Abstract: There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.

65 citations


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  • ...Consequently there has been increased interest in developing statistical and computational tools for inferring such 'transmission trees' from various types of data, including times of symptom onset, contact tracing data, spatial data and, increasingly frequently, pathogen whole genome sequence (WGS) data [5] [6] [7] [8] [9] [10] [11] [12] [13] ....

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TL;DR: With the commercialization of a third generation of sequencing technologies imminent, this work discusses the applications of current next-generation sequencing methods and explores their impact on and contribution to microbial genome research.
Abstract: Next-generation sequencing technologies have had a dramatic impact in the field of genomic research through the provision of a low cost, high-throughput alternative to traditional capillary sequencers. These new sequencing methods have surpassed their original scope and now provide a range of utility-based applications, which allow for a more comprehensive analysis of the structure and content of microbial genomes than was previously possible. With the commercialization of a third generation of sequencing technologies imminent, we discuss the applications of current next-generation sequencing methods and explore their impact on and contribution to microbial genome research.

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TL;DR: The first phylogenomics analysis for harvestmen is presented, derived from comparative RNA-Seq data for eight species representing all suborders, and overwhelmingly support the “classical” hypothesis of Opiliones relationships, including the higher-level clades Palpatores and Phalangida.
Abstract: Next-generation sequencing technologies are rapidly transforming molecular systematic studies of non-model animal taxa. The arachnid order Opiliones (commonly known as “harvestmen”) includes more than 6,400 described species placed into four well-supported lineages (suborders). Fossil plus molecular clock evidence indicates that these lineages were diverging in the late Silurian to mid-Carboniferous, with some fossil harvestmen representing the earliest known land animals. Perhaps because of this ancient divergence, phylogenetic resolution of subordinal interrelationships within Opiliones has been difficult. We present the first phylogenomics analysis for harvestmen, derived from comparative RNA-Seq data for eight species representing all suborders. Over 30 gigabases of original Illumina short-read data were used in de novo assemblies, resulting in 50–80,000 transcripts per taxon. Transcripts were compared to published scorpion and tick genomics data, and a stringent filtering process was used to identify over 350 putatively single-copy, orthologous protein-coding genes shared among taxa. Phylogenetic analyses using various partitioning strategies, data coding schemes, and analytical methods overwhelmingly support the “classical” hypothesis of Opiliones relationships, including the higher-level clades Palpatores and Phalangida. Relaxed molecular clock analyses using multiple alternative fossil calibration strategies corroborate ancient divergences within Opiliones that are possibly deeper than the recorded fossil record indicates. The assembled data matrices, comprising genes that are conserved, highly expressed, and varying in length and phylogenetic informativeness, represent an important resource for future molecular systematic studies of Opiliones and other arachnid groups.

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06 Jul 2016-PLOS ONE
TL;DR: It is concluded that complement and complement-related genes play important roles during defense against A. hydrophila infection and the immune response is activated at 4 h after the bacterial injections, indicating that the complement pathways are activated at the early stage of bacterial infection.
Abstract: The grass carp (Ctenopharyngodon idella) is an important commercial farmed herbivorous fish species in China, but is susceptible to Aeromonas hydrophila infections. In the present study, we performed de novo RNA-Seq sequencing of spleen tissue from specimens of a disease-resistant family, which were given intra-peritoneal injections containing PBS with or without a dose of A. hydrophila. The fish were sampled from the control group at 0 h, and from the experimental group at 4, 8, 12, 24, 48 and 72 h. 122.18 million clean reads were obtained from the normalized cDNA libraries; these were assembled into 425,260 contigs and then 191,795 transcripts. Of those, 52,668 transcripts were annotated with the NCBI Nr database, and 41,347 of the annotated transcripts were assigned into 90 functional groups. 20,569 unigenes were classified into six main categories, including 38 secondary KEGG pathways. 2,992 unigenes were used in the analysis of differentially expressed genes (DEGs). 89 of the putative DEGs were related to the immune system and 41 of them were involved in the complement and coagulation cascades pathway. This study provides insights into the complement and complement-related pathways involved in innate immunity, through expression profile analysis of the genomic resources in C. idella. We conclude that complement and complement-related genes play important roles during defense against A. hydrophila infection. The immune response is activated at 4 h after the bacterial injections, indicating that the complement pathways are activated at the early stage of bacterial infection. The study has improved our understanding of the immune response mechanisms in C. idella to bacterial pathogens.

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References
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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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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.
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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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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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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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