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Zhang Y, Wu Z, Lu S, Lin M, Yue X, Wang Z, Cai B. Time-Series Expression Profile Analysis of Post-Traumatic Joint Contracture in Rats at the Early Stages of the Healing Process. J Inflamm Res 2023; 16:1169-1181. [PMID: 36945316 PMCID: PMC10024884 DOI: 10.2147/jir.s400557] [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: 12/07/2022] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Objective This study aimed to characterize the gene expression profile at the early stages of the healing process of post-traumatic joint contracture (PTJC). Methods Twelve rats were used for PTJC model establishment and were divided into four groups according to the sampling time: S0d, S3d, S7d and S2w. Transcriptome sequencing was performed on fibrotic joint capsule samples in four groups followed by bioinformatics analyses including differentially expressed genes (DEGs) screening, Short Time-series Expression Miner (STEM) analysis, network construction, and pathway analysis. Five important genes were validated by qRT-PCR. Results A total of 1171, 1052 and 793 DEGs were screened in S3d vs S0d, S7d vs S0d, and S2w vs S0d comparison groups, respectively. A total of 383 overlapping genes were screened out, which were significantly enriched in some inflammatory functions and pathways. Through STEM analysis, three clusters were identified, including 105, 57 and 57 DEGs, respectively. Then, based on the cluster genes, 10 genes, such as Il6, Timp1, Cxcl1, Cxcr4 and Mmp3, were further selected after PPI and pathway analyses. The expression levels of Il6, Timp1, Cxcl1, Cxcr4 and Mmp3 were validated by qRT-PCR. Conclusion The present study screened out several genes with significant changes in expression levels at the early stages of the healing process in PTJC, such as Il6, Timp1, Cxcl1, Cxcr4 and Mmp3. Our study offers a valuable contribution to the understanding pathomechanism of PTJC.
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Affiliation(s)
- Yuxin Zhang
- Department of Rehabilitation Medicine, Hainan Western Central Hospital, Danzhou, Hainan, People’s Republic of China
- Department of Rehabilitation Medicine, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
- Department of Oral Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology, Shanghai, People’s Republic of China
- Correspondence: Yuxin Zhang; Bin Cai, Department of Rehabilitation Medicine, Hainan Western Central Hospital, No. 2, Fubo East Road, Nada Town, Danzhou, Hainan, 571700, People’s Republic of China, Tel +86-21-53315248, Email ;
| | - Zhigang Wu
- Department of Rehabilitation Medicine, Hainan Western Central Hospital, Danzhou, Hainan, People’s Republic of China
| | - Shenji Lu
- Department of Rehabilitation Medicine, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Minghui Lin
- Department of Rehabilitation Medicine, Hainan Western Central Hospital, Danzhou, Hainan, People’s Republic of China
| | - Xiaokun Yue
- Shanghai Key Laboratory of Orthopedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Zengguang Wang
- Shanghai Key Laboratory of Orthopedic Implants, Department of Orthopaedic Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Bin Cai
- Department of Rehabilitation Medicine, Hainan Western Central Hospital, Danzhou, Hainan, People’s Republic of China
- Department of Rehabilitation Medicine, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
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John A, Muenzen K, Ausmees K. Evaluation of serverless computing for scalable execution of a joint variant calling workflow. PLoS One 2021; 16:e0254363. [PMID: 34242357 PMCID: PMC8270184 DOI: 10.1371/journal.pone.0254363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 06/24/2021] [Indexed: 11/18/2022] Open
Abstract
Advances in whole-genome sequencing have greatly reduced the cost and time of obtaining raw genetic information, but the computational requirements of analysis remain a challenge. Serverless computing has emerged as an alternative to using dedicated compute resources, but its utility has not been widely evaluated for standardized genomic workflows. In this study, we define and execute a best-practice joint variant calling workflow using the SWEEP workflow management system. We present an analysis of performance and scalability, and discuss the utility of the serverless paradigm for executing workflows in the field of genomics research. The GATK best-practice short germline joint variant calling pipeline was implemented as a SWEEP workflow comprising 18 tasks. The workflow was executed on Illumina paired-end read samples from the European and African super populations of the 1000 Genomes project phase III. Cost and runtime increased linearly with increasing sample size, although runtime was driven primarily by a single task for larger problem sizes. Execution took a minimum of around 3 hours for 2 samples, up to nearly 13 hours for 62 samples, with costs ranging from $2 to $70.
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Affiliation(s)
- Aji John
- Department of Biology, University of Washington, Seattle, Washington, United States of America
- * E-mail:
| | - Kathleen Muenzen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America
| | - Kristiina Ausmees
- Department of Information Technology, Uppsala University, Uppsala, Sweden
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Kotlarz K, Mielczarek M, Suchocki T, Czech B, Guldbrandtsen B, Szyda J. The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines. J Appl Genet 2020; 61:607-616. [PMID: 32996082 PMCID: PMC7652806 DOI: 10.1007/s13353-020-00586-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/11/2020] [Accepted: 09/18/2020] [Indexed: 11/18/2022]
Abstract
A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models.
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Affiliation(s)
- Krzysztof Kotlarz
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Magda Mielczarek
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland.,Institute of Animal Breeding, Balice, Poland
| | - Tomasz Suchocki
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland.,Institute of Animal Breeding, Balice, Poland
| | - Bartosz Czech
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland
| | - Bernt Guldbrandtsen
- Animal Breeding Group, Department of Animal Sciences, University of Bonn, Bonn, Germany
| | - Joanna Szyda
- Biostatistics Group, Department of Genetics, Wroclaw University of Environmental and Life Sciences, Kozuchowska 7, 51-631, Wroclaw, Poland. .,Institute of Animal Breeding, Balice, Poland.
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