1
|
Vega JM, Podio M, Orjuela J, Siena LA, Pessino SC, Combes MC, Mariac C, Albertini E, Pupilli F, Ortiz JPA, Leblanc O. Chromosome-scale genome assembly and annotation of Paspalum notatum Flüggé var. saurae. Sci Data 2024; 11:891. [PMID: 39152143 PMCID: PMC11329641 DOI: 10.1038/s41597-024-03731-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 08/02/2024] [Indexed: 08/19/2024] Open
Abstract
Paspalum notatum Flüggé is an economically important subtropical fodder grass that is widely used in the Americas. Here, we report a new chromosome-scale genome assembly and annotation of a diploid biotype collected in the center of origin of the species. Using Oxford Nanopore long reads, we generated a 557.81 Mb genome assembly (N50 = 56.1 Mb) with high gene completeness (BUSCO = 98.73%). Genome annotation identified 320 Mb (57.86%) of repetitive elements and 45,074 gene models, of which 36,079 have a high level of confidence. Further characterisation included the identification of 59 miRNA precursors together with their putative targets. The present work provides a comprehensive genomic resource for P. notatum improvement and a reference frame for functional and evolutionary research within the genus.
Collapse
Grants
- PUE 22920160100043CO Consejo Nacional de Investigaciones Científicas y Técnicas (National Scientific and Technical Research Council)
- PIP 11220200101680CO Consejo Nacional de Investigaciones Científicas y Técnicas (National Scientific and Technical Research Council)
- PICT 2019 3414 Ministry of Science, Technology and Productive Innovation, Argentina | Agencia Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology, Argentina)
- PICT 2019-02153 Ministry of Science, Technology and Productive Innovation, Argentina | Agencia Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology, Argentina)
- PICT-2017-1956 Ministry of Science, Technology and Productive Innovation, Argentina | Agencia Nacional de Promoción Científica y Tecnológica (National Agency for Science and Technology, Argentina)
- PIP 80020190300021UR Universidad Nacional de Rosario (National University of Rosario)
- 101007438 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions)
- 872417 EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions)
- PRIN 2022Z4HLLJ Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research)
- P2022KFJB5 Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research)
- Ministero dell'Istruzione, dell'Università e della Ricerca (Ministry of Education, University and Research)
Collapse
Affiliation(s)
- Juan Manuel Vega
- Laboratorio de Biología Molecular, Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR) CONICET-UNR, Facultad de Ciencias Agrarias, Campo Experimental Villarino, Universidad Nacional de Rosario, Zavalla (S2125ZAA), Santa Fe, Argentina
| | - Maricel Podio
- Laboratorio de Biología Molecular, Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR) CONICET-UNR, Facultad de Ciencias Agrarias, Campo Experimental Villarino, Universidad Nacional de Rosario, Zavalla (S2125ZAA), Santa Fe, Argentina
| | - Julie Orjuela
- DIADE, Univ. Montpellier, CIRAD, IRD, Montpellier, France
| | - Lorena A Siena
- Laboratorio de Biología Molecular, Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR) CONICET-UNR, Facultad de Ciencias Agrarias, Campo Experimental Villarino, Universidad Nacional de Rosario, Zavalla (S2125ZAA), Santa Fe, Argentina
| | - Silvina C Pessino
- Laboratorio de Biología Molecular, Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR) CONICET-UNR, Facultad de Ciencias Agrarias, Campo Experimental Villarino, Universidad Nacional de Rosario, Zavalla (S2125ZAA), Santa Fe, Argentina
| | | | - Cedric Mariac
- DIADE, Univ. Montpellier, CIRAD, IRD, Montpellier, France
| | - Emidio Albertini
- Department of Agricultural, Food and Environmental Science, University of Perugia, 06121, Perugia, Italy
| | - Fulvio Pupilli
- Institute of Biosciences and Bioresources (IBBR), National Research Council (CNR), 06128, Perugia, Italy
| | - Juan Pablo A Ortiz
- Laboratorio de Biología Molecular, Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR) CONICET-UNR, Facultad de Ciencias Agrarias, Campo Experimental Villarino, Universidad Nacional de Rosario, Zavalla (S2125ZAA), Santa Fe, Argentina.
| | | |
Collapse
|
2
|
Ling X, Pan L, Li L, Huang Y, Wang C, Huang C, Long Y, Zhai N, Xiao Q, Luo J, Tang R, Meng L, Huang Y. Detection of hemophilia A genetic variants using third-generation long-read sequencing. Clin Chim Acta 2024; 562:119884. [PMID: 39038592 DOI: 10.1016/j.cca.2024.119884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/18/2024] [Accepted: 07/18/2024] [Indexed: 07/24/2024]
Abstract
BACKGROUND Hemophilia A (HA) is an X-linked recessive genetic disorder caused by pathogenic variations of the factor VIII -encoding gene, F8 gene. Due to the large size and diverse types of variations in the F8 gene, causative mutations in F8 cannot be simultaneously detected in one step by traditional molecular analysis, and genetic molecular diagnosis and prenatal screening of HA still face significant difficulties and challenges in clinical practice. Therefore, we aimed to develop and validate an efficient, accurate, and time-saving method for the genetic detection of HA. METHODS A comprehensive analysis of hemophilia A (CAHEA) method based on long-range PCR and long-read sequencing (LRS) was used to detect F8 gene mutations in 14 clinical HA samples. The LRS results were compared with those of the conventional methods to evaluate the accuracy and sensitivity of the proposed approach. RESULTS The CAHEA method successfully identified 14 F8 variants in all probands, including 3 small insertion deletions, 4 single nucleotide variants, and 7 intron 22 inversions in a "one-step" manner, of which 2 small deletions have not been reported previously. Moreover, this method provided an opportunity to analyze the mechanism of rearrangement and the pathogenicity of F8 variants. The LRS results were validated and found to be in 100% agreement with those obtained using the conventional method. CONCLUSION Our proposed LRS-based F8 gene detection method is an accurate and reproducible genetic screening and diagnostic method with significant clinical value. It provides efficient, comprehensive, and accurate genetic screening and diagnostic services for individuals at high risk of HA as well as for premarital and prenatal populations.
Collapse
Affiliation(s)
- Xiaoting Ling
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Liqiu Pan
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Linlin Li
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Yunhua Huang
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Chenghan Wang
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Chaoyu Huang
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Yan Long
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Ningneng Zhai
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Qingxing Xiao
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Jiaqi Luo
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Rongheng Tang
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Li Meng
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China
| | - Yifang Huang
- Department of Clinical Laboratory, the First Affiliated Hospital of Guangxi Medical University, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, Nanning 530021, China.
| |
Collapse
|
3
|
Liu S, Obert C, Yu YP, Zhao J, Ren BG, Liu JJ, Wiseman K, Krajacich BJ, Wang W, Metcalfe K, Smith M, Ben-Yehezkel T, Luo JH. Utility analyses of AVITI sequencing chemistry. BMC Genomics 2024; 25:778. [PMID: 39127634 DOI: 10.1186/s12864-024-10686-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. RESULTS Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. CONCLUSION These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.
Collapse
Affiliation(s)
- Silvia Liu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA.
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
| | - Caroline Obert
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Yan-Ping Yu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Junhua Zhao
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Bao-Guo Ren
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Jia-Jun Liu
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA
| | - Kelly Wiseman
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Benjamin J Krajacich
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Wenjia Wang
- Department of Biostatistics, University of Pittsburgh School of Public Health, Pittsburgh, USA
| | - Kyle Metcalfe
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Mat Smith
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Tuval Ben-Yehezkel
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA, 92121, USA
| | - Jian-Hua Luo
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15261, USA.
- High Throughput Genome Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, Pittsburgh, USA.
| |
Collapse
|
4
|
Loving R, Sullivan DK, Reese F, Rebboah E, Sakr J, Rezaie N, Liang HY, Filimban G, Kawauchi S, Oakes C, Trout D, Williams BA, MacGregor G, Wold BJ, Mortazavi A, Pachter L. Long-read sequencing transcriptome quantification with lr-kallisto. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604364. [PMID: 39071335 PMCID: PMC11275803 DOI: 10.1101/2024.07.19.604364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
RNA abundance quantification has become routine and affordable thanks to high-throughput "short-read" technologies that provide accurate molecule counts at the gene level. Similarly accurate and affordable quantification of definitive full-length, transcript isoforms has remained a stubborn challenge, despite its obvious biological significance across a wide range of problems. "Long-read" sequencing platforms now produce data-types that can, in principle, drive routine definitive isoform quantification. However some particulars of contemporary long-read datatypes, together with isoform complexity and genetic variation, present bioinformatic challenges. We show here, using ONT data, that fast and accurate quantification of long-read data is possible and that it is improved by exome capture. To perform quantifications we developed lr-kallisto, which adapts the kallisto bulk and single-cell RNA-seq quantification methods for long-read technologies.
Collapse
Affiliation(s)
- Rebekah Loving
- Division of Biology and Biological Engineering, California Institute of Technology, USA
| | - Delaney K. Sullivan
- Division of Biology and Biological Engineering, California Institute of Technology, USA
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Fairlie Reese
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Elisabeth Rebboah
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Jasmine Sakr
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Narges Rezaie
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Heidi Y. Liang
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Ghassan Filimban
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Shimako Kawauchi
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
| | - Conrad Oakes
- Division of Biology and Biological Engineering, California Institute of Technology, USA
| | - Diane Trout
- Division of Biology and Biological Engineering, California Institute of Technology, USA
| | - Brian A. Williams
- Division of Biology and Biological Engineering, California Institute of Technology, USA
| | - Grant MacGregor
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
| | - Barbara J. Wold
- Division of Biology and Biological Engineering, California Institute of Technology, USA
| | - Ali Mortazavi
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, USA
| |
Collapse
|
5
|
Rodrigues J, Alfieri R, Bione S, Azzalin CM. TERRA ONTseq: a long-read-based sequencing pipeline to study the human telomeric transcriptome. RNA (NEW YORK, N.Y.) 2024; 30:955-966. [PMID: 38777382 PMCID: PMC11251519 DOI: 10.1261/rna.079906.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/05/2024] [Indexed: 05/25/2024]
Abstract
The long noncoding RNA TERRA is transcribed from telomeres in virtually all eukaryotes with linear chromosomes. In humans, TERRA transcription is driven in part by promoters comprising CpG dinucleotide-rich repeats of 29 bp repeats, believed to be present in half of the subtelomeres. Thus far, TERRA expression has been analyzed mainly using molecular biology-based approaches that only generate partial and somehow biased results. Here, we present a novel experimental pipeline to study human TERRA based on long-read sequencing (TERRA ONTseq). By applying TERRA ONTseq to different cell lines, we show that the vast majority of human telomeres produce TERRA and that the cellular levels of TERRA transcripts vary according to their chromosomes of origin. Using TERRA ONTseq, we also identified regions containing TERRA transcription start sites (TSSs) in more than half of human subtelomeres. TERRA TSS regions are generally found immediately downstream from 29 bp repeat-related sequences, which appear to be more widespread than previously estimated. Finally, we isolated a novel TERRA promoter from the highly expressed subtelomere of the long arm of Chromosome 7. With the development of TERRA ONTseq, we provide a refined picture of human TERRA biogenesis and expression and we equip the scientific community with an invaluable tool for future studies.
Collapse
Affiliation(s)
- Joana Rodrigues
- Instituto de Medicina Molecular João Lobo Antunes (iMM), Faculdade de Medicina da Universidade de Lisboa, Lisbon 1649-028, Portugal
| | - Roberta Alfieri
- Istituto di Genetica Molecolare Luigi Luca Cavalli-Sforza, Consiglio Nazionale delle Ricerche, Pavia 27100, Italy
- Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, Segrate (MI) 20054, Italy
| | - Silvia Bione
- Istituto di Genetica Molecolare Luigi Luca Cavalli-Sforza, Consiglio Nazionale delle Ricerche, Pavia 27100, Italy
| | - Claus M Azzalin
- Instituto de Medicina Molecular João Lobo Antunes (iMM), Faculdade de Medicina da Universidade de Lisboa, Lisbon 1649-028, Portugal
| |
Collapse
|
6
|
Liu S, Obert C, Yu YP, Zhao J, Ren BG, Liu JJ, Wiseman K, Krajacich BJ, Wang W, Metcalfe K, Smith M, Ben-Yehezkel T, Luo JH. Utility Analyses of AVITI Sequencing Chemistry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.590136. [PMID: 38712138 PMCID: PMC11071311 DOI: 10.1101/2024.04.18.590136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. Results Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. Conclusion These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.
Collapse
Affiliation(s)
- Silvia Liu
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| | - Caroline Obert
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Yan-Ping Yu
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| | - Junhua Zhao
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Bao-Guo Ren
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
| | - Jia-Jun Liu
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| | - Kelly Wiseman
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Benjamin J Krajacich
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Wenjia Wang
- Department of Biostatistics, University of Pittsburgh School of Public Health, United States
| | - Kyle Metcalfe
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Mat Smith
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Tuval Ben-Yehezkel
- Element Biosciences Inc, 10055 Barnes Canyon Road, Suite 100, San Diego, CA 92121, United States
| | - Jian-Hua Luo
- Department of Pathology, University of Pittsburgh School of Medicine, United States
- High Throughput Genome Center, University of Pittsburgh School of Medicine, United States
- Pittsburgh Liver Research Center, University of Pittsburgh School of Medicine, United States
| |
Collapse
|
7
|
Edelbroek B, Kjellin J, Jerlström-Hultqvist J, Koskiniemi S, Söderbom F. Chromosome-level genome assembly and annotation of the social amoeba Dictyostelium firmibasis. Sci Data 2024; 11:678. [PMID: 38909042 PMCID: PMC11193728 DOI: 10.1038/s41597-024-03513-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 06/12/2024] [Indexed: 06/24/2024] Open
Abstract
Dicytostelium firmibasis is a member of Dictyostelia, a group of social amoebae that upon starvation display aggregative multicellularity where the amoebae transition from uni- to multicellular life. The D. firmibasis genome assembly that is currently available is of limited use due to its low contiguity, large number of undetermined bases, and lack of annotations. Here we used Nanopore long read sequencing, complemented with Illumina sequencing, and developmental transcriptomics as well as small RNA-sequencing, to present a new, fully annotated, chromosome-level D. firmibasis genome assembly. The new assembly contains no undetermined bases, and consists mainly of six large contigs representing the chromosomes, as well as a complete mitochondrial genome. This new genome assembly will be a valuable tool, allowing comprehensive comparison to Dictyostelium discoideum, the dictyostelid genetically tractable model. Further, the new genome will be important for studies of evolutionary processes governing the transition from unicellular to multicellular organisms and will aid in the sequencing and annotation of other dictyostelids genomes, many of which are currently of poor quality.
Collapse
Affiliation(s)
- Bart Edelbroek
- Department of Cell and Molecular Biology, BMC, Uppsala University, SE-751 24, Uppsala, Sweden.
| | - Jonas Kjellin
- Department of Cell and Molecular Biology, BMC, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Jon Jerlström-Hultqvist
- Department of Cell and Molecular Biology, BMC, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Sanna Koskiniemi
- Department of Cell and Molecular Biology, BMC, Uppsala University, SE-751 24, Uppsala, Sweden
| | - Fredrik Söderbom
- Department of Cell and Molecular Biology, BMC, Uppsala University, SE-751 24, Uppsala, Sweden.
| |
Collapse
|
8
|
Sivaprakasam N, Vaithiyanathan S, Gandhi K, Narayanan S, Kavitha PS, Rajasekaran R, Muthurajan R. Metagenomics approaches in unveiling the dynamics of Plant Growth-Promoting Microorganisms (PGPM) vis-à-vis Phytophthora sp. suppression in various crop ecological systems. Res Microbiol 2024:104217. [PMID: 38857835 DOI: 10.1016/j.resmic.2024.104217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 05/02/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024]
Abstract
Phytophthora species are destructive pathogens causing yield losses in different ecological systems, such as potato, black pepper, pepper, avocado, citrus, and tobacco. The diversity of plant growth-promoting microorganisms (PGPM) plays a crucial role in disease suppression. Knowledge of metagenomics approaches is essential for assessing the dynamics of PGPM and Phytophthora species across various ecosystems, facilitating effective management strategies for better crop protection. This review discusses the dynamic interplay between PGPM and Phytophthora sp. using metagenomics approaches that sheds light on the potential of PGPM strains tailored to specific crop ecosystems to bolster pathogen suppressiveness.
Collapse
Affiliation(s)
- Navarasu Sivaprakasam
- Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | | | - Karthikeyan Gandhi
- Department of Plant Pathology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - Swarnakumari Narayanan
- Department of Nematology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - P S Kavitha
- School of Post Graduate Studies, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - Raghu Rajasekaran
- Centre for Plant Molecular Biology & Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| | - Raveendran Muthurajan
- Centre for Plant Molecular Biology & Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
| |
Collapse
|
9
|
Szakállas N, Barták BK, Valcz G, Nagy ZB, Takács I, Molnár B. Can long-read sequencing tackle the barriers, which the next-generation could not? A review. Pathol Oncol Res 2024; 30:1611676. [PMID: 38818014 PMCID: PMC11137202 DOI: 10.3389/pore.2024.1611676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/30/2024] [Indexed: 06/01/2024]
Abstract
The large-scale heterogeneity of genetic diseases necessitated the deeper examination of nucleotide sequence alterations enhancing the discovery of new targeted drug attack points. The appearance of new sequencing techniques was essential to get more interpretable genomic data. In contrast to the previous short-reads, longer lengths can provide a better insight into the potential health threatening genetic abnormalities. Long-reads offer more accurate variant identification and genome assembly methods, indicating advances in nucleotide deflect-related studies. In this review, we introduce the historical background of sequencing technologies and show their benefits and limits, as well. Furthermore, we highlight the differences between short- and long-read approaches, including their unique advances and difficulties in methodologies and evaluation. Additionally, we provide a detailed description of the corresponding bioinformatics and the current applications.
Collapse
Affiliation(s)
- Nikolett Szakállas
- Department of Biological Physics, Faculty of Science, Eötvös Loránd University, Budapest, Hungary
| | - Barbara K. Barták
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Gábor Valcz
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
- HUN-REN-SU Translational Extracellular Vesicle Research Group, Budapest, Hungary
| | - Zsófia B. Nagy
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - István Takács
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Béla Molnár
- Department of Internal Medicine and Oncology, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| |
Collapse
|
10
|
Xu Z, Qu HQ, Chan J, Kao C, Hakonarson H, Wang K. Single-Cell Omics for Transcriptome CHaracterization (SCOTCH): isoform-level characterization of gene expression through long-read single-cell RNA sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.590597. [PMID: 38746128 PMCID: PMC11092450 DOI: 10.1101/2024.04.29.590597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
The advent of long-read single-cell transcriptome sequencing (lr-scRNA-Seq) represents a significant leap forward in single-cell genomics. With the recent introduction of R10 flowcells by Oxford Nanopore, we propose that previous computational methods designed to handle high sequencing error rates are no longer relevant, and that the prevailing approach using short reads to compile "barcode space" (candidate barcode list) to de-multiplex long reads are no longer necessary. Instead, computational methods should now shift focus on harnessing the unique benefits of long reads to analyze transcriptome complexity. In this context, we introduce a comprehensive suite of computational methods named Single-Cell Omics for Transcriptome CHaracterization (SCOTCH). Our method is compatible with the single-cell library preparation platform from both 10X Genomics and Parse Biosciences, facilitating the analysis of special cell populations, such as neurons, hepatocytes and developing cardiomyocytes. We specifically re-formulated the transcript mapping problem with a compatibility matrix and addressed the multiple-mapping issue using probabilistic inference, which allows the discovery of novel isoforms as well as the detection of differential isoform usage between cell populations. We evaluated SCOTCH through analysis of real data across different combinations of single-cell libraries and sequencing technologies (10X + Illumina, Parse + Illumina, 10X + Nanopore_R9, 10X + Nanopore_R10, Parse + Nanopore_R10), and showed its ability to infer novel biological insights on cell type-specific isoform expression. These datasets enhance the availability of publicly available data for continued development of computational approaches. In summary, SCOTCH allows extraction of more biological insights from the new advancements in single-cell library construction and sequencing technologies, facilitating the examination of transcriptome complexity at the single-cell level.
Collapse
Affiliation(s)
- Zhuoran Xu
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Hui-Qi Qu
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Joe Chan
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Charlly Kao
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
| | - Hakon Hakonarson
- The Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, 19104, USA
- Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| |
Collapse
|
11
|
Pidaparti M, Geddes GC, Durbin MD. Clinical Genetic and Genomic Testing in Congenital Heart Disease and Cardiomyopathy. J Clin Med 2024; 13:2544. [PMID: 38731073 PMCID: PMC11084871 DOI: 10.3390/jcm13092544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/20/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
Congenital heart disease (CHD) and cardiomyopathies are the leading cause of morbidity and mortality worldwide. These conditions are often caused by genetic factors, and recent research has shown that genetic and genomic testing can provide valuable information for patient care. By identifying genetic causes, healthcare providers can screen for other related health conditions, offer early interventions, estimate prognosis, select appropriate treatments, and assess the risk for family members. Genetic and genomic testing is now the standard of care in patients with CHD and cardiomyopathy. However, rapid advances in technology and greater availability of testing options have led to changes in recommendations for the most appropriate testing method. Several recent studies have investigated the utility of genetic testing in this changing landscape. This review summarizes the literature surrounding the clinical utility of genetic evaluation in patients with CHD and cardiomyopathy.
Collapse
Affiliation(s)
- Mahati Pidaparti
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Gabrielle C. Geddes
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Matthew D. Durbin
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
- Herman B Wells Center for Pediatric Research, 1044 W. Walnut, Indianapolis, IN 46202, USA
| |
Collapse
|
12
|
Xie L, Gong X, Yang K, Huang Y, Zhang S, Shen L, Sun Y, Wu D, Ye C, Zhu QH, Fan L. Technology-enabled great leap in deciphering plant genomes. NATURE PLANTS 2024; 10:551-566. [PMID: 38509222 DOI: 10.1038/s41477-024-01655-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Plant genomes provide essential and vital basic resources for studying many aspects of plant biology and applications (for example, breeding). From 2000 to 2020, 1,144 genomes of 782 plant species were sequenced. In the past three years (2021-2023), 2,373 genomes of 1,031 plant species, including 793 newly sequenced species, have been assembled, representing a great leap. The 2,373 newly assembled genomes, of which 63 are telomere-to-telomere assemblies and 921 have been generated in pan-genome projects, cover the major phylogenetic clades. Substantial advances in read length, throughput, accuracy and cost-effectiveness have notably simplified the achievement of high-quality assemblies. Moreover, the development of multiple software tools using different algorithms offers the opportunity to generate more complete and complex assemblies. A database named N3: plants, genomes, technologies has been developed to accommodate the metadata associated with the 3,517 genomes that have been sequenced from 1,575 plant species since 2000. We also provide an outlook for emerging opportunities in plant genome sequencing.
Collapse
Affiliation(s)
- Lingjuan Xie
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
- Hainan Institute of Zhejiang University, Yazhou Bay, Shanya, China
| | - Xiaojiao Gong
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Kun Yang
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Yujie Huang
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Shiyu Zhang
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Leti Shen
- Hainan Institute of Zhejiang University, Yazhou Bay, Shanya, China
| | - Yanqing Sun
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Dongya Wu
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Chuyu Ye
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Qian-Hao Zhu
- CSIRO Agriculture and Food, Black Mountain Laboratories, Canberra, Australia
| | - Longjiang Fan
- Institute of Crop Sciences & Institute of Bioinformatics, Zhejiang University, Hangzhou, China.
- Hainan Institute of Zhejiang University, Yazhou Bay, Shanya, China.
| |
Collapse
|
13
|
Naidu AS, Wang CK, Rao P, Mancini F, Clemens RA, Wirakartakusumah A, Chiu HF, Yen CH, Porretta S, Mathai I, Naidu SAG. Precision nutrition to reset virus-induced human metabolic reprogramming and dysregulation (HMRD) in long-COVID. NPJ Sci Food 2024; 8:19. [PMID: 38555403 PMCID: PMC10981760 DOI: 10.1038/s41538-024-00261-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 03/15/2024] [Indexed: 04/02/2024] Open
Abstract
SARS-CoV-2, the etiological agent of COVID-19, is devoid of any metabolic capacity; therefore, it is critical for the viral pathogen to hijack host cellular metabolic machinery for its replication and propagation. This single-stranded RNA virus with a 29.9 kb genome encodes 14 open reading frames (ORFs) and initiates a plethora of virus-host protein-protein interactions in the human body. These extensive viral protein interactions with host-specific cellular targets could trigger severe human metabolic reprogramming/dysregulation (HMRD), a rewiring of sugar-, amino acid-, lipid-, and nucleotide-metabolism(s), as well as altered or impaired bioenergetics, immune dysfunction, and redox imbalance in the body. In the infectious process, the viral pathogen hijacks two major human receptors, angiotensin-converting enzyme (ACE)-2 and/or neuropilin (NRP)-1, for initial adhesion to cell surface; then utilizes two major host proteases, TMPRSS2 and/or furin, to gain cellular entry; and finally employs an endosomal enzyme, cathepsin L (CTSL) for fusogenic release of its viral genome. The virus-induced HMRD results in 5 possible infectious outcomes: asymptomatic, mild, moderate, severe to fatal episodes; while the symptomatic acute COVID-19 condition could manifest into 3 clinical phases: (i) hypoxia and hypoxemia (Warburg effect), (ii) hyperferritinemia ('cytokine storm'), and (iii) thrombocytosis (coagulopathy). The mean incubation period for COVID-19 onset was estimated to be 5.1 days, and most cases develop symptoms after 14 days. The mean viral clearance times were 24, 30, and 39 days for acute, severe, and ICU-admitted COVID-19 patients, respectively. However, about 25-70% of virus-free COVID-19 survivors continue to sustain virus-induced HMRD and exhibit a wide range of symptoms that are persistent, exacerbated, or new 'onset' clinical incidents, collectively termed as post-acute sequelae of COVID-19 (PASC) or long COVID. PASC patients experience several debilitating clinical condition(s) with >200 different and overlapping symptoms that may last for weeks to months. Chronic PASC is a cumulative outcome of at least 10 different HMRD-related pathophysiological mechanisms involving both virus-derived virulence factors and a multitude of innate host responses. Based on HMRD and virus-free clinical impairments of different human organs/systems, PASC patients can be categorized into 4 different clusters or sub-phenotypes: sub-phenotype-1 (33.8%) with cardiac and renal manifestations; sub-phenotype-2 (32.8%) with respiratory, sleep and anxiety disorders; sub-phenotype-3 (23.4%) with skeleto-muscular and nervous disorders; and sub-phenotype-4 (10.1%) with digestive and pulmonary dysfunctions. This narrative review elucidates the effects of viral hijack on host cellular machinery during SARS-CoV-2 infection, ensuing detrimental effect(s) of virus-induced HMRD on human metabolism, consequential symptomatic clinical implications, and damage to multiple organ systems; as well as chronic pathophysiological sequelae in virus-free PASC patients. We have also provided a few evidence-based, human randomized controlled trial (RCT)-tested, precision nutrients to reset HMRD for health recovery of PASC patients.
Collapse
Affiliation(s)
- A Satyanarayan Naidu
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA.
- N-terminus Research Laboratory, 232659 Via del Rio, Yorba Linda, CA, 92887, USA.
| | - Chin-Kun Wang
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- School of Nutrition, Chung Shan Medical University, 110, Section 1, Jianguo North Road, Taichung, 40201, Taiwan
| | - Pingfan Rao
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- College of Food and Bioengineering, Fujian Polytechnic Normal University, No.1, Campus New Village, Longjiang Street, Fuqing City, Fujian, China
| | - Fabrizio Mancini
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- President-Emeritus, Parker University, 2540 Walnut Hill Lane, Dallas, TX, 75229, USA
| | - Roger A Clemens
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- University of Southern California, Alfred E. Mann School of Pharmacy/D. K. Kim International Center for Regulatory & Quality Sciences, 1540 Alcazar St., CHP 140, Los Angeles, CA, 90089, USA
| | - Aman Wirakartakusumah
- International Union of Food Science and Technology (IUFoST), Guelph, ON, Canada
- IPMI International Business School Jakarta; South East Asian Food and Agriculture Science and Technology, IPB University, Bogor, Indonesia
| | - Hui-Fang Chiu
- Department of Chinese Medicine, Taichung Hospital, Ministry of Health & Well-being, Taichung, Taiwan
| | - Chi-Hua Yen
- Department of Family and Community Medicine, Chung Shan Medical University Hospital; School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Sebastiano Porretta
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- President, Italian Association of Food Technology (AITA), Milan, Italy
- Experimental Station for the Food Preserving Industry, Department of Consumer Science, Viale Tanara 31/a, I-43121, Parma, Italy
| | - Issac Mathai
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- Soukya International Holistic Health Center, Whitefield, Bengaluru, India
| | - Sreus A G Naidu
- Global Nutrition Healthcare Council (GNHC) Mission-COVID, Yorba Linda, CA, USA
- N-terminus Research Laboratory, 232659 Via del Rio, Yorba Linda, CA, 92887, USA
| |
Collapse
|
14
|
Odle E, Kahng S, Riewluang S, Kurihara K, Wakeman KC. GINSA: an accumulator for paired locality and next-generation small ribosomal subunit sequence data. Bioinformatics 2024; 40:btae152. [PMID: 38502961 PMCID: PMC10987208 DOI: 10.1093/bioinformatics/btae152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/15/2024] [Accepted: 03/16/2024] [Indexed: 03/21/2024] Open
Abstract
MOTIVATION Motivated by the challenges of decentralized genetic data spread across multiple international organizations, GINSA leverages the Global Biodiversity Information Facility infrastructure to automatically retrieve and link small ribosomal subunit sequences with locality information. RESULTS Testing on taxa from major organism groups demonstrates broad applicability across taxonomic levels and dataset sizes. AVAILABILITY AND IMPLEMENTATION GINSA is a freely accessible Python program under the MIT License and can be installed from PyPI via pip.
Collapse
Affiliation(s)
- Eric Odle
- Department of Natural History Sciences, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Samuel Kahng
- Department of Oceanography, University of Hawaii at Manoa, Honolulu, HI 96822, United States
- Institute for the Advancement of Higher Education, Hokkaido University, Sapporo, Hokkaido 060-0817, Japan
| | - Siratee Riewluang
- Department of Natural History Sciences, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Kyoko Kurihara
- Department of Natural History Sciences, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| | - Kevin C Wakeman
- Institute for the Advancement of Higher Education, Hokkaido University, Sapporo, Hokkaido 060-0817, Japan
- Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
| |
Collapse
|
15
|
Hurabielle C, LaFlam TN, Gearing M, Ye CJ. Functional genomics in inborn errors of immunity. Immunol Rev 2024; 322:53-70. [PMID: 38329267 PMCID: PMC10950534 DOI: 10.1111/imr.13309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Inborn errors of immunity (IEI) comprise a diverse spectrum of 485 disorders as recognized by the International Union of Immunological Societies Committee on Inborn Error of Immunity in 2022. While IEI are monogenic by definition, they illuminate various pathways involved in the pathogenesis of polygenic immune dysregulation as in autoimmune or autoinflammatory syndromes, or in more common infectious diseases that may not have a significant genetic basis. Rapid improvement in genomic technologies has been the main driver of the accelerated rate of discovery of IEI and has led to the development of innovative treatment strategies. In this review, we will explore various facets of IEI, delving into the distinctions between PIDD and PIRD. We will examine how Mendelian inheritance patterns contribute to these disorders and discuss advancements in functional genomics that aid in characterizing new IEI. Additionally, we will explore how emerging genomic tools help to characterize new IEI as well as how they are paving the way for innovative treatment approaches for managing and potentially curing these complex immune conditions.
Collapse
Affiliation(s)
- Charlotte Hurabielle
- Division of Rheumatology, Department of Medicine, UCSF, San Francisco, California, USA
| | - Taylor N LaFlam
- Division of Pediatric Rheumatology, Department of Pediatrics, UCSF, San Francisco, California, USA
| | - Melissa Gearing
- Division of Rheumatology, Department of Medicine, UCSF, San Francisco, California, USA
| | - Chun Jimmie Ye
- Institute for Human Genetics, UCSF, San Francisco, California, USA
- Institute of Computational Health Sciences, UCSF, San Francisco, California, USA
- Gladstone Genomic Immunology Institute, San Francisco, California, USA
- Parker Institute for Cancer Immunotherapy, UCSF, San Francisco, California, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, California, USA
- Department of Microbiology and Immunology, UCSF, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California, USA
- Arc Institute, Palo Alto, California, USA
| |
Collapse
|
16
|
Zhang D, Jia N, Hu Z, Keqing Z, Chenxi S, Chunying S, Chen C, Chen W, Hu Y, Ruan Z. Bioinformatics identification of potential biomarkers and therapeutic targets for ischemic stroke and vascular dementia. Exp Gerontol 2024; 187:112374. [PMID: 38320734 DOI: 10.1016/j.exger.2024.112374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/18/2024] [Accepted: 01/30/2024] [Indexed: 02/09/2024]
Abstract
Ischemic stroke and vascular dementia, as common cerebrovascular diseases, with the former causing irreversible neurological damage and the latter causing cognitive and memory impairment, are closely related and have long received widespread attention. Currently, the potential causative genes of these two diseases have yet to be investigated, and effective early diagnostic tools for the diseases have not yet emerged. In this study, we screened new potential biomarkers and analyzed new therapeutic targets for both diseases from the perspective of immune infiltration. Two gene expression profiles on ischemic stroke and vascular dementia were obtained from the NCBI GEO database, and key genes were identified by LASSO regression and SVM-RFE algorithms, and key genes were analyzed by GO and KEGG enrichment. The CIBERSORT algorithm was applied to the gene expression profile species of the two diseases to quantify the 24 subpopulations of immune cells. Moreover, logistic regression modeling analysis was applied to illustrate the stability of the key genes in the diagnosis. Finally, the key genes were validated using RT-PCR assay. A total of 105 intersecting DEGs genes were obtained in the 2 sets of GEO datasets, and bioinformatics functional analysis of the intersecting DEGs genes showed that GO was mainly involved in the purine ribonucleoside triphosphate metabolic process,respiratory chain complex,DNA-binding transcription factor binding and active transmembrane transporter activity. KEGG is mainly involved in the Oxidative phosphorylation, cAMP signaling pathway. The LASSO regression algorithm and SVM-RFE algorithm finally obtained three genes, GAS2L1, ARHGEF40 and PFKFB3, and the logistic regression prediction model determined that the three genes, GAS2L1 (AUC: 0.882), ARHGEF40 (AUC: 0.867) and PFKFB3 (AUC: 0.869), had good diagnostic performance. Meanwhile, the two disease core genes and immune infiltration were closely related, GAS2L1 and PFKFB3 had the highest positive correlation with macrophage M1 (p < 0.001) and the highest negative correlation with mast cell activation (p = 0.0017); ARHGEF40 had the highest positive correlation with macrophage M1 and B cells naive (p < 0.001), the highest negative correlation with B cell memory highest correlation (p = 0.0047). RT-PCR results showed that the relative mRNA expression levels of GAS2L1, ARHGEF40, and PFKFB3 were significantly elevated in the populations of both disease groups (p < 0.05). Immune infiltration-based models can be used to predict the diagnosis of patients with ischemic stroke and vascular dementia and provide a new perspective on the early diagnosis and treatment of both diseases.
Collapse
Affiliation(s)
- Ding Zhang
- Guangxi university of chinese medicine Nanning, China
| | - Ni Jia
- Shaanxi University of Traditional Chinese Medicine Xianyang, China
| | - Zhihan Hu
- Shanghai University of Traditional Chinese Medicine Shanghai, China
| | - Zhou Keqing
- Guangxi university of chinese medicine Nanning, China
| | - Song Chenxi
- Guangxi university of chinese medicine Nanning, China
| | - Sun Chunying
- Guangxi university of chinese medicine Nanning, China
| | - Canrong Chen
- Guangxi university of chinese medicine Nanning, China
| | - Wei Chen
- Guangxi university of chinese medicine First Affiliated Hospital Nanning, China
| | - Yueqiang Hu
- Guangxi university of chinese medicine First Affiliated Hospital Nanning, China.
| | - Ziyun Ruan
- Guangxi university of chinese medicine Nanning, China
| |
Collapse
|
17
|
Sami A, El-Metwally S, Rashad MZ. MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads. BMC Bioinformatics 2024; 25:61. [PMID: 38321434 PMCID: PMC10848413 DOI: 10.1186/s12859-024-05681-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/29/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. RESULTS We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. CONCLUSIONS This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.
Collapse
Affiliation(s)
- Amira Sami
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, Egypt
| | - Sara El-Metwally
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, Egypt.
- Biomedical Informatics Department, Faculty of Computer Science and Engineering, New Mansoura University, Gamasa, 35712, Egypt.
| | - M Z Rashad
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, P.O. Box: 35516, Mansoura, Egypt
| |
Collapse
|
18
|
Mathew MT, Babcock M, Hou YCC, Hunter JM, Leung ML, Mei H, Schieffer K, Akkari Y. Clinical Cytogenetics: Current Practices and Beyond. J Appl Lab Med 2024; 9:61-75. [PMID: 38167757 DOI: 10.1093/jalm/jfad086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/17/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Throughout history, the field of cytogenetics has witnessed significant changes due to the constant evolution of technologies used to assess chromosome number and structure. Similar to the evolution of single nucleotide variant detection from Sanger sequencing to next-generation sequencing, the identification of chromosome alterations has progressed from banding to fluorescence in situ hybridization (FISH) to chromosomal microarrays. More recently, emerging technologies such as optical genome mapping and genome sequencing have made noteworthy contributions to clinical laboratory testing in the field of cytogenetics. CONTENT In this review, we journey through some of the most pivotal discoveries that have shaped the development of clinical cytogenetics testing. We also explore the current test offerings, their uses and limitations, and future directions in technology advancements. SUMMARY Cytogenetics methods, including banding and targeted assessments like FISH, continue to hold crucial roles in cytogenetic testing. These methods offer a rapid turnaround time, especially for conditions with a known etiology involving recognized cytogenetic aberrations. Additionally, laboratories have the flexibility to now employ higher-throughput methodologies to enhance resolution for cases with greater complexity.
Collapse
Affiliation(s)
- Mariam T Mathew
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
- Department of Pediatrics, The Ohio State University, Columbus, OH, United States
| | - Melanie Babcock
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
| | - Ying-Chen Claire Hou
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
| | - Jesse M Hunter
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
| | - Marco L Leung
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
- Department of Pediatrics, The Ohio State University, Columbus, OH, United States
| | - Hui Mei
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
| | - Kathleen Schieffer
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
- Department of Pediatrics, The Ohio State University, Columbus, OH, United States
| | - Yassmine Akkari
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, OH, United States
- Department of Pathology, The Ohio State University, Columbus, OH, United States
| |
Collapse
|
19
|
Oehler JB, Wright H, Stark Z, Mallett AJ, Schmitz U. The application of long-read sequencing in clinical settings. Hum Genomics 2023; 17:73. [PMID: 37553611 PMCID: PMC10410870 DOI: 10.1186/s40246-023-00522-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/01/2023] [Indexed: 08/10/2023] Open
Abstract
Long-read DNA sequencing technologies have been rapidly evolving in recent years, and their ability to assess large and complex regions of the genome makes them ideal for clinical applications in molecular diagnosis and therapy selection, thereby providing a valuable tool for precision medicine. In the third-generation sequencing duopoly, Oxford Nanopore Technologies and Pacific Biosciences work towards increasing the accuracy, throughput, and portability of long-read sequencing methods while trying to keep costs low. These trades have made long-read sequencing an attractive tool for use in research and clinical settings. This article provides an overview of current clinical applications and limitations of long-read sequencing and explores its potential for point-of-care testing and health care in remote settings.
Collapse
Affiliation(s)
- Josephine B Oehler
- Biomedical Sciences and Molecular Biology, College of Public Health, Medical & Vet Sciences, James Cook University, Townsville, Australia
- College of Medicine and Dentistry, James Cook University, Townsville, Australia
| | - Helen Wright
- Nursing and Midwifery, College of Healthcare Sciences, James Cook University, Townsville, Australia
| | - Zornitza Stark
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, Australia
- University of Melbourne, Melbourne, Australia
- Australian Genomics, Melbourne, Australia
| | - Andrew J Mallett
- College of Medicine and Dentistry, James Cook University, Townsville, Australia
- Department of Renal Medicine, Townsville University Hospital, Townsville, Australia
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Ulf Schmitz
- Biomedical Sciences and Molecular Biology, College of Public Health, Medical & Vet Sciences, James Cook University, Townsville, Australia.
- Centre for Tropical Bioinformatics and Molecular Biology, Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, Australia.
- Computational BioMedicine Lab Centenary Institute, The University of Sydney, Camperdown, Australia.
- Faculty of Medicine & Health, The University of Sydney, Camperdown, Australia.
| |
Collapse
|
20
|
Abondio P, Cilli E, Luiselli D. Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference. Life (Basel) 2023; 13:1360. [PMID: 37374141 DOI: 10.3390/life13061360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/02/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.
Collapse
Affiliation(s)
- Paolo Abondio
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
| | - Elisabetta Cilli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
| | - Donata Luiselli
- Laboratory of Ancient DNA, Department of Cultural Heritage, University of Bologna, Via degli Ariani 1, 48121 Ravenna, Italy
| |
Collapse
|