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GRABBAG! STEPHANIE J SPIELMAN, PHD BIO5312, FALL 2017 REGULAR - PowerPoint PPT Presentation

Dec 03, 2023 •376 likes •629 views

GRABBAG! STEPHANIE J SPIELMAN, PHD BIO5312, FALL 2017 REGULAR EXPRESSIONS Pattern-based search and replace Extremely powerful beyond all reason Excellent for text (file) manipulation! CRITICAL PSA: TEXT EDITORS Microsoft

  1. GRABBAG! STEPHANIE J SPIELMAN, PHD BIO5312, FALL 2017

  2. REGULAR EXPRESSIONS • Pattern-based search and replace • Extremely powerful beyond all reason • Excellent for text (file) manipulation!

  3. CRITICAL PSA: TEXT EDITORS • Microsoft Word is not a text editor!!!!!!! I’m so serious!!! • GUI • TextEdit and Notepad • Textwrangler/BBEdit for Macs • Sublime 3 for everyone else • Newer, awesome one called Atom • CLI • Vim/vi, emacs, nano, pico (b/c puns) • https://en.wikipedia.org/wiki/Editor_war

  4. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Mus Regex: Mus Match: Match: Mus Mus musculus musculus

  5. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Mus Regex: Mus musculus musculus Match: Match: Mus Mus musculus musculus

  6. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Regex: [mM mM]us us Match: Match: Mus Mus musculus musculus

  7. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Regex: [A [A-Za Za-z] z]us us Match: Match: Mus Mus musculus musculus

  8. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Regex: \wus us Match: Match: Mus Mus musculus musculus

  9. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Regex: \w+ w+ Match: Match: Mus Mus musculus musculus

  10. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Regex: [A [A-Z] Z]\w+ w+ \w+ w+ Match: Match: Mus Mus musculus musculus

  11. REGULAR EXPRESSIONS String: Mus String: Mus musculus musculus Regex: Regex: ([A [A-Z] Z])\w+ w+ (\w+ w+) Replace: Replace: \1. 1. \2 New string: M. New string: M. musculus musculus

  12. REGULAR EXPRESSIONS String: 85.34 cm String: 85.34 cm Regex: Regex: \d+ d+ Match: Match: 85.34 85.34 cm cm

  13. REGULAR EXPRESSIONS String: 85.34 cm String: 85.34 cm Regex: Regex: \d+ d+\.\d+ d+ Match: Match: 85.34 85.34 cm cm

  14. REGULAR EXPRESSIONS String: 85.34 cm String: 85.34 cm Regex: Regex: \d+ d+\.\d+ d+ \w+ w+ Match: Match: 85.34 85.34 cm cm

  15. REGULAR EXPRESSIONS String: 85 cm String: 85 cm Regex: Regex: \d+ d+\.\d+ d+ \w+ w+ Match: 85 cm Match: 85 cm

  16. REGULAR EXPRESSIONS String: 85 cm String: 85 cm Regex: Regex: \d+ d+\.* .*\d* d* \w+ w+ Match: 85 cm Match: 85 cm

  17. REGULAR EXPRESSIONS String: 85 cm String: 85 cm Regex: Regex: ^\d Match: 85 cm Match: 85 cm

  18. REGULAR EXPRESSIONS String: 85 cm String: 85 cm Regex: Regex: \w$ w$ Match: 85 cm Match: 85 cm

  19. REGULAR EXPRESSIONS String: 85.341234 cm String: 85.341234 cm Regex: Regex: (\d+ d+\.\d{3} d{3})\d+ cm d+ cm Replace: Replace: \1 New string: 85.341 New string: 85.341

  20. REGULAR EXPRESSIONS String: 85.34 cm String: 85.34 cm Regex: Regex: (\d+ d+\.\d{3} d{3})\d+ cm d+ cm Replace: Replace: \1 New string: ????? New string: ?????

  21. GROUP EXERCISE Come up with a regular expression to convert the following text: 85.34 cm 85.34 cm 85.3 cm 85.3 cm 85.678 cm 85.678 cm 85.6 cm 85.6 cm 923.1115 cm 923.1115 cm 923.1 cm 923.1 cm 1.95 cm 1.95 cm 1.9 cm 1.9 cm 6 cm 6 cm 6 cm 6 cm

  22. BREAK

  23. � � � � � � � � Reference Sequence Software genome data setup Sequence quality checks Steps 1 and 2 Collect metadata for Steps 3–6 experiment Mapping reads, Alternative organize files, alignment Steps 7–12 Transcript inspect mapping (SAM/BAM files) annotation Alternative Feature counting counting Step 13 (count table) Data structures, Step 14 normalization, edgeR DESeq fitness checks 2-group differential GLM-based differential comparison comparisons Inspect and save results Additional sanity Step 15 checks

  24. USE A SPLICE-AWARE ALIGNER https://genomebiology.biomedcentral.com/articles/10.1186/s13059 -016-0881-8

  25. ALIGNERS AND PSEUDO- PROTOCOL THIS IS Transcript-level expression analysis of RNA-seq THE NEW experiments with HISAT, StringTie and Ballgown TOPHAT2 Mihaela Pertea 1,2 , Daehwan Kim 1 , Geo M Pertea 1 , Jeffrey T Leek 3 & Steven L Salzberg 1–4 Sequence analysis STAR: ultrafast universal RNA-seq aligner Alexander Dobin 1, *, Carrie A. Davis 1 , Felix Schlesinger 1 , Jorg Drenkow 1 , Chris Zaleski 1 , Sonali Jha 1 , Philippe Batut 1 , Mark Chaisson 2 and Thomas R. Gingeras 1 1 Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA and 2 Pacific Biosciences, Menlo Park, CA, USA Associate Editor: Inanc Birol Near-optimal probabilistic RNA-seq quantification Nicolas L Bray 1 , Harold Pimentel 2 , Páll Melsted 3 & Lior Pachter 2,4,5 We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.

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