Last updated: 2023-11-24
Checks: 7 0
Knit directory: bioinformatics_tips/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200503) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version ba521a7. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Untracked files:
Untracked: analysis/framework.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/compsci.Rmd) and HTML
(docs/compsci.html) files. If you’ve configured a remote
Git repository (see ?wflow_git_remote), click on the
hyperlinks in the table below to view the files as they were in that
past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ba521a7 | Dave Tang | 2023-11-24 | Computer science |
Computer science concerns the study of computation, automation, and information. Knowledge of some theory and principles in computer science will help you write better programs and pipelines.
Separation of concerns is a design principle for separating a computer program into distinct sections. Each section addresses a separate concern, such as pre-processing data or training a model, and leads to a modular program. This separation makes it easier to read and maintain code. While we can keep separating a task into finer details, it is useful in bioinformatics to keep code organised in specific tasks of a workflow/pipeline.
In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform.
Most commonly used classifications, sorted from the best (most efficient) to worst (least efficient):
From The Biostar Handbook:
Refactoring is an iterative process of improving code to reduce its redundancy and make it more generic and simpler. The reason we need refactoring is that typically a process becomes more well understood as we work and solve it. Periodically, we may need to revisit previous steps and rework them to match the later steps. Refactoring may feel wasteful, as often we modify what may seem to already be working just fine. But in any analysis, complexity can be a hindrance. Refactoring takes on complexity and attempts to simplify our work and will pay dividends in the future. Refactoring takes some practice and typically the returns are diminishing – that is, every code can be refactored, but after a few rounds of doing so the benefits are typically much smaller.
A bit array is an array data structure that compactly stores bits. It can be used to implement a simple set data structure. A bit array is effective at exploiting bit-level parallelism in hardware to perform operations quickly. A typical bit array stores kw bits, where w is the number of bits in the unit of storage, such as a byte or word, and k is some nonnegative integer. If w does not divide the number of bits to be stored, some space is wasted due to internal fragmentation.
This is a very compact way of storing data and is used to store properties of reads in BAM files.
samtools flagsAbout: Convert between textual and numeric flag representation
Usage: samtools flags FLAGS...
Each FLAGS argument is either an INT (in decimal/hexadecimal/octal) representing
a combination of the following numeric flag values, or a comma-separated string
NAME,...,NAME representing a combination of the following flag names:
0x1 1 PAIRED paired-end / multiple-segment sequencing technology
0x2 2 PROPER_PAIR each segment properly aligned according to aligner
0x4 4 UNMAP segment unmapped
0x8 8 MUNMAP next segment in the template unmapped
0x10 16 REVERSE SEQ is reverse complemented
0x20 32 MREVERSE SEQ of next segment in template is rev.complemented
0x40 64 READ1 the first segment in the template
0x80 128 READ2 the last segment in the template
0x100 256 SECONDARY secondary alignment
0x200 512 QCFAIL not passing quality controls or other filters
0x400 1024 DUP PCR or optical duplicate
0x800 2048 SUPPLEMENTARY supplementary alignment
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vctrs_0.6.4 httr_1.4.7 cli_3.6.1 knitr_1.44
[5] rlang_1.1.1 xfun_0.40 stringi_1.7.12 processx_3.8.2
[9] promises_1.2.1 jsonlite_1.8.7 glue_1.6.2 rprojroot_2.0.3
[13] git2r_0.32.0 htmltools_0.5.6.1 httpuv_1.6.12 ps_1.7.5
[17] sass_0.4.7 fansi_1.0.5 rmarkdown_2.25 jquerylib_0.1.4
[21] tibble_3.2.1 evaluate_0.22 fastmap_1.1.1 yaml_2.3.7
[25] lifecycle_1.0.3 whisker_0.4.1 stringr_1.5.0 compiler_4.3.2
[29] fs_1.6.3 pkgconfig_2.0.3 Rcpp_1.0.11 rstudioapi_0.15.0
[33] later_1.3.1 digest_0.6.33 R6_2.5.1 utf8_1.2.4
[37] pillar_1.9.0 callr_3.7.3 magrittr_2.0.3 bslib_0.5.1
[41] tools_4.3.2 cachem_1.0.8 getPass_0.2-2