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Emerging Applications for Next Generation Sequencing
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Emerging Applications for Next Generation Sequencing

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (30 April 2018) | Viewed by 40441

Special Issue Editors

Prof. Dr. Jan Komorowski

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Guest Editor
Department of Cell and Molecular Biology, Uppsala University and Institute of Computer Science, Polish Academy of Sciences, 01-248 Warsaw, Poland
Prof. Dr. Bozena Kaminska

E-Mail Website
Guest Editor
The Nencki Institute of Experimental Biology, Polish Academy of Sciences, 03‑131 Warszawa, Poland
Dr. Manfred Grabherr

E-Mail Website
Guest Editor
Department of Cell and Molecular Biology, Uppsala University, 752 36 Uppsala, Sweden

Special Issue Information

Dear Colleagues,

There has been an extraordinary progress in Next Generation Sequencing (NGS) since the completion of the Human Genome Project, yet the original goal that it would be the ultimate basis to curing human diseases has not been reached. Huge amounts of data have been, and are being, created by NGS and related technologies, yet we are still far from delivering on the original promise. Sequencing technologies will continue to evolve, get better and cheaper. Whatever is regarded the standard today will vanish tomorrow, merely to be replaced by more efficient technologies. The holy grail, however, lies in unraveling the information that is contained in the data.

We are just beginning to realize that living systems are so complex that only methods of Artificial Intelligence (AI), particularly machine learning, can help us make significant progress in this endeavor.

With this Special Issue, we invite researchers to present their recent and novel approaches to exploring all-omics data by developing and applying AI methods to Life Sciences. The suggested topics include, but are not limited to, answering the challenges of simultaneously analyzing heterogeneous types of data sets, such as mutations, gene expressions, DNA-protein interactions, methylation, or metabolomics. Likewise, we welcome submissions of studies exploring experiment planning and design using methods from machine intelligence research. While the costs are dropping, and turn-over time of experiments decrease, and the portability of instruments make them increasingly applicable in clinical settings and in the field (notably the application of MinION in the 2014 Ebola outbreak), understanding of the multivariate nature of non-Mendelian diseases will remain the main challenge for precision medicine. We thus invite articles reporting on novel applications of AI methods that explore NGS in connection to other sources of data, with the aim of finding new drugs and promoting better treatments.

Similarly, an exploration of new areas such as, for instance, synthetic biology, in the context of NGS data and application of AI techniques of, e.g., knowledge-based planning, and innovative approaches that combine unstructured text and health data with NGS are also encouraged.

Prof. Dr. Jan Komorowski
Prof. Dr. Bozena Kaminska
Dr. Manfred Grabherr
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Next Generation Sequencing
  • Artificial Intelligence
  • Machine learning
  • Merging genomic data
  • Precision medicine
  • Experiment planning
  • Synthetic biology
  • Knowledge-based design
  • Unstructured text
  • Medical records

Published Papers (6 papers)

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Editorial

Jump to: Research, Review

4 pages, 173 KiB  
Editorial
Special Issue Introduction: The Wonders and Mysteries Next Generation Sequencing Technologies Help Reveal
by Manfred G. Grabherr, Bozena Kaminska and Jan Komorowski
Genes 2018, 9(10), 505; https://doi.org/10.3390/genes9100505 - 18 Oct 2018
Viewed by 3330
Abstract
The massive increase in computational power over the recent years and wider applications
of machine learning methods, coincidental or not, were paralleled by remarkable advances in
high-throughput DNA sequencing technologies.[...] Full article
(This article belongs to the Special Issue Emerging Applications for Next Generation Sequencing)

Research

Jump to: Editorial, Review

11 pages, 424 KiB  
Article
Validation of Ion TorrentTM Inherited Disease Panel with the PGMTM Sequencing Platform for Rapid and Comprehensive Mutation Detection
by Abeer E. Mustafa, Tariq Faquih, Batoul Baz, Rana Kattan, Abdulelah Al-Issa, Asma I. Tahir, Faiqa Imtiaz, Khushnooda Ramzan, Moeenaldeen Al-Sayed, Mohammed Alowain, Zuhair Al-Hassnan, Hamad Al-Zaidan, Mohamed Abouelhoda, Bashayer R. Al-Mubarak and Nada A. Al Tassan
Genes 2018, 9(5), 267; https://doi.org/10.3390/genes9050267 - 22 May 2018
Cited by 9 | Viewed by 6776
Abstract
Quick and accurate molecular testing is necessary for the better management of many inherited diseases. Recent technological advances in various next generation sequencing (NGS) platforms, such as target panel-based sequencing, has enabled comprehensive, quick, and precise interrogation of many genetic variations. As a [...] Read more.
Quick and accurate molecular testing is necessary for the better management of many inherited diseases. Recent technological advances in various next generation sequencing (NGS) platforms, such as target panel-based sequencing, has enabled comprehensive, quick, and precise interrogation of many genetic variations. As a result, these technologies have become a valuable tool for gene discovery and for clinical diagnostics. The AmpliSeq Inherited Disease Panel (IDP) consists of 328 genes underlying more than 700 inherited diseases. Here, we aimed to assess the performance of the IDP as a sensitive and rapid comprehensive gene panel testing. A total of 88 patients with inherited diseases and causal mutations that were previously identified by Sanger sequencing were randomly selected for assessing the performance of the IDP. The IDP successfully detected 93.1% of the mutations in our validation cohort, achieving high overall gene coverage (98%). The sensitivity for detecting single nucleotide variants (SNVs) and short Indels was 97.3% and 69.2%, respectively. IDP, when coupled with Ion Torrent Personal Genome Machine (PGM), delivers comprehensive and rapid sequencing for genes that are responsible for various inherited diseases. Our validation results suggest the suitability of this panel for use as a first-line screening test after applying the necessary clinical validation. Full article
(This article belongs to the Special Issue Emerging Applications for Next Generation Sequencing)
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19 pages, 4320 KiB  
Article
Identification of Major Rhizobacterial Taxa Affected by a Glyphosate-Tolerant Soybean Line via Shotgun Metagenomic Approach
by Gui-Hua Lu, Xiao-Mei Hua, Li Liang, Zhong-Ling Wen, Mei-Hang Du, Fan-Fan Meng, Yan-Jun Pang, Jin-Liang Qi, Cheng-Yi Tang and Yong-Hua Yang
Genes 2018, 9(4), 214; https://doi.org/10.3390/genes9040214 - 16 Apr 2018
Cited by 8 | Viewed by 5787
Abstract
The worldwide commercial cultivation of transgenic crops, including glyphosate-tolerant (GT) soybeans, has increased widely during the past 20 years. However, it is accompanied with a growing concern about potential effects of transgenic crops on the soil microbial communities, especially on rhizosphere bacterial communities. [...] Read more.
The worldwide commercial cultivation of transgenic crops, including glyphosate-tolerant (GT) soybeans, has increased widely during the past 20 years. However, it is accompanied with a growing concern about potential effects of transgenic crops on the soil microbial communities, especially on rhizosphere bacterial communities. Our previous study found that the GT soybean line NZL06-698 (N698) significantly affected rhizosphere bacteria, including some unidentified taxa, through 16S rRNA gene (16S rDNA) V4 region amplicon deep sequencing via Illumina MiSeq. In this study, we performed 16S rDNA V5–V7 region amplicon deep sequencing via Illumina MiSeq and shotgun metagenomic approaches to identify those major taxa. Results of these processes revealed that the species richness and evenness increased in the rhizosphere bacterial communities of N698, the beta diversity of the rhizosphere bacterial communities of N698 was affected, and that certain dominant bacterial phyla and genera were related to N698 compared with its control cultivar Mengdou12. Consistent with our previous findings, this study showed that N698 affects the rhizosphere bacterial communities. In specific, N698 negatively affects Rahnella, Janthinobacterium, Stenotrophomonas, Sphingomonas and Luteibacter while positively affecting Arthrobacter, Bradyrhizobium, Ramlibacter and Nitrospira. Full article
(This article belongs to the Special Issue Emerging Applications for Next Generation Sequencing)
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Review

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12 pages, 666 KiB  
Review
Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications
by Daniela Barros-Silva, C. Joana Marques, Rui Henrique and Carmen Jerónimo
Genes 2018, 9(9), 429; https://doi.org/10.3390/genes9090429 - 23 Aug 2018
Cited by 97 | Viewed by 11139
Abstract
DNA methylation is an epigenetic modification that plays a pivotal role in regulating gene expression and, consequently, influences a wide variety of biological processes and diseases. The advances in next-generation sequencing technologies allow for genome-wide profiling of methyl marks both at a single-nucleotide [...] Read more.
DNA methylation is an epigenetic modification that plays a pivotal role in regulating gene expression and, consequently, influences a wide variety of biological processes and diseases. The advances in next-generation sequencing technologies allow for genome-wide profiling of methyl marks both at a single-nucleotide and at a single-cell resolution. These profiling approaches vary in many aspects, such as DNA input, resolution, coverage, and bioinformatics analysis. Thus, the selection of the most feasible method according with the project’s purpose requires in-depth knowledge of those techniques. Currently, high-throughput sequencing techniques are intensively used in epigenomics profiling, which ultimately aims to find novel biomarkers for detection, diagnosis prognosis, and prediction of response to therapy, as well as to discover new targets for personalized treatments. Here, we present, in brief, a portrayal of next-generation sequencing methodologies’ evolution for profiling DNA methylation, highlighting its potential for translational medicine and presenting significant findings in several diseases. Full article
(This article belongs to the Special Issue Emerging Applications for Next Generation Sequencing)
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19 pages, 832 KiB  
Review
High-Throughput Approaches onto Uncover (Epi)Genomic Architecture of Type 2 Diabetes
by Anna Dziewulska, Aneta M. Dobosz and Agnieszka Dobrzyn
Genes 2018, 9(8), 374; https://doi.org/10.3390/genes9080374 - 26 Jul 2018
Cited by 14 | Viewed by 5774
Abstract
Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified [...] Read more.
Type 2 diabetes (T2D) is a complex disorder that is caused by a combination of genetic, epigenetic, and environmental factors. High-throughput approaches have opened a new avenue toward a better understanding of the molecular bases of T2D. A genome-wide association studies (GWASs) identified a group of the most common susceptibility genes for T2D (i.e., TCF7L2, PPARG, KCNJ1, HNF1A, PTPN1, and CDKAL1) and illuminated novel disease-causing pathways. Next-generation sequencing (NGS)-based techniques have shed light on rare-coding genetic variants that account for an appreciable fraction of T2D heritability (KCNQ1 and ADRA2A) and population risk of T2D (SLC16A11, TPCN2, PAM, and CCND2). Moreover, single-cell sequencing of human pancreatic islets identified gene signatures that are exclusive to α-cells (GCG, IRX2, and IGFBP2) and β-cells (INS, ADCYAP1, INS-IGF2, and MAFA). Ongoing epigenome-wide association studies (EWASs) have progressively defined links between epigenetic markers and the transcriptional activity of T2D target genes. Differentially methylated regions were found in TCF7L2, THADA, KCNQ1, TXNIP, SOCS3, SREBF1, and KLF14 loci that are related to T2D. Additionally, chromatin state maps in pancreatic islets were provided and several non-coding RNAs (ncRNA) that are key to T2D pathogenesis were identified (i.e., miR-375). The present review summarizes major progress that has been made in mapping the (epi)genomic landscape of T2D within the last few years. Full article
(This article belongs to the Special Issue Emerging Applications for Next Generation Sequencing)
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21 pages, 1582 KiB  
Review
Decoding the Heart through Next Generation Sequencing Approaches
by Michal Pawlak, Katarzyna Niescierowicz and Cecilia Lanny Winata
Genes 2018, 9(6), 289; https://doi.org/10.3390/genes9060289 - 07 Jun 2018
Cited by 12 | Viewed by 6809
Abstract
Vertebrate organs develop through a complex process which involves interaction between multiple signaling pathways at the molecular, cell, and tissue levels. Heart development is an example of such complex process which, when disrupted, results in congenital heart disease (CHD). This complexity necessitates a [...] Read more.
Vertebrate organs develop through a complex process which involves interaction between multiple signaling pathways at the molecular, cell, and tissue levels. Heart development is an example of such complex process which, when disrupted, results in congenital heart disease (CHD). This complexity necessitates a holistic approach which allows the visualization of genome-wide interaction networks, as opposed to assessment of limited subsets of factors. Genomics offers a powerful solution to address the problem of biological complexity by enabling the observation of molecular processes at a genome-wide scale. The emergence of next generation sequencing (NGS) technology has facilitated the expansion of genomics, increasing its output capacity and applicability in various biological disciplines. The application of NGS in various aspects of heart biology has resulted in new discoveries, generating novel insights into this field of study. Here we review the contributions of NGS technology into the understanding of heart development and its disruption reflected in CHD and discuss how emerging NGS based methodologies can contribute to the further understanding of heart repair. Full article
(This article belongs to the Special Issue Emerging Applications for Next Generation Sequencing)
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