MARC details
| 000 -LEADER |
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| fixed length control field |
04028nam a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
201210b2023 a|||f bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
EG-CaNU |
| Transcribing agency |
EG-CaNU |
| 041 0# - Language Code |
|---|
| Language code of text |
eng |
| Language code of abstract |
eng |
| -- |
ara |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
610 |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohamed Omar Moawad Fares |
| 245 1# - TITLE STATEMENT |
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| Title |
BENCHMARKING SEVERAL CLUSTERING AND DENOISING APPROACHES FOR OTUs/ASVs INFERENCE FROM AMPLICON BASED SEQUENCING/ |
| Statement of responsibility, etc. |
Mohamed Omar Moawad Fares |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Date of publication, distribution, etc. |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
74 p. |
| Other physical details |
ill. |
| Dimensions |
21 cm. |
| 500 ## - GENERAL NOTE |
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| Materials specified |
Supervisor: Mohamed El-Helw |
| 502 ## - Dissertation Note |
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| Dissertation type |
Thesis (M.A.)—Nile University, Egypt, 2023 . |
| 504 ## - Bibliography |
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| Bibliography |
"Includes bibliographical references" |
| 505 0# - Contents |
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| Formatted contents note |
Contents:<br/>Dedication................................................................................................................... iii<br/>Acknowledgments....................................................................................................... iv<br/>List of Tables .............................................................................................................. vi<br/>List of Figures............................................................................................................ vii<br/>Abstract..................................................................................................................... viii<br/>Introduction................................................................................................................... 1<br/>Background .................................................................................................................. 4<br/>Methods ...................................................................................................................... 18<br/>Results......................................................................................................................... 32<br/>Discussion................................................................................................................... 44<br/>References................................................................................................................... 53 |
| 520 3# - Abstract |
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| Abstract |
Abstract:<br/>Amplicon sequencing is an indispensable tool for microbiome studies <br/>needed to unravel the taxonomical composition and relative abundance of <br/>microbial community. Yet, several artifacts are introduced at different <br/>processing steps, including sequencing errors necessitating the use of <br/>computational methods to eliminate those errors. Distance-based <br/>clustering into operational taxonomic units (OTUs) and sequence reads <br/>denoising into Amplicon Sequence Variants (ASVs) are two main <br/>approaches to handle this issue. Varying experimental setups and complex <br/>pipeline parameters have hindered unbiased comparisons between <br/>different approaches, resulting in divergent findings across separate <br/>studies. In this study, we aimed to conduct a comprehensive benchmarking <br/>analysis via an unbiased head-to-head comparison of eight different <br/>clustering and denoising algorithms by using a collection of various mocks <br/>from the Mockrobiota database. Using unified preprocessing steps for <br/>quality filtering and chimera removal, a fair comparison between DADA2, <br/>Deblur, MED, UNOISE3, UPARSE, DGC, Average neighborhood and <br/>Opticlust was conducted. DADA2 and UPARSE were the most efficient<br/>algorithms, producing comparable results in terms of overall error rate, <br/>percentage of exact matches to the mock reference and percentage of <br/>taxonomical over-splitting and over-merging. These results suggest that at <br/>the same level of quality preprocessing, sequence abundance filtering and <br/>chimera detection parameters, OTU clustering and ASV denoising <br/>produce comparable results with minor approach-dependent traits.<br/>Keywords: Amplicon Sequence Analysis, Denoising, Clustering, <br/>OTU, ASV, Benchmarking |
| 546 ## - Language Note |
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| Language Note |
Text in English, abstracts in English and Arabic |
| 650 #4 - Subject |
|---|
| Subject |
Informatics-IFM |
| 655 #7 - Index Term-Genre/Form |
|---|
| Source of term |
NULIB |
| focus term |
Dissertation, Academic |
| 690 ## - Subject |
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| School |
Informatics-IFM |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Thesis |
| 650 #4 - Subject |
|---|
| -- |
266 |
| 655 #7 - Index Term-Genre/Form |
|---|
| -- |
187 |
| 690 ## - Subject |
|---|
| -- |
266 |