bioSyntax: syntax highlighting for computational biology

Babaian, Artem; Ebou, Anicet; Fegen, Alyssa; Kam, Ho Yin; Novakovsky, Gherman; Wong, Jasper; Aïssi, Dylan; Li, Yao

doi:10.1186/s12859-018-2315-y

Cited by 3 publications

(3 citation statements)

References 39 publications

(32 reference statements)

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“…As multiple sequence alignment problems have no objective mathematical function to optimize, most practitioners modify the computed output manually, or ever perform de novo manual alignments. In our case, the multiple alignment was completed by hand using the bioSyntax [ 14 ] highlighting package in the vim editor. We found that although UTRs showed a few similarities within certain ranges, the 5’-UTR segments varied greatly within the first 1000 bases.…”

Section: Methods and Resultsmentioning

confidence: 99%

Pattern matching for high precision detection of LINE-1s in human genomes

et al. 2022

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Background Long interspersed element 1 (LINE-1 or L1) retrotransposons are mobile elements that constitute 17–20% of the human genome. Strong correlations between abnormal L1 expression and several human diseases have been reported. This has motivated increasing interest in accurate quantification of the number of L1 copies present in any given biologic specimen. A main obstacle toward this aim is that L1s are relatively long DNA segments with regions of high variability, or largely present in the human genome as truncated fragments. These particularities render traditional alignment strategies, such as seed-and-extend inefficient, as the number of segments that are similar to L1s explodes exponentially. This study uses the pattern matching methodology for more accurate identification of L1s. We validate experimentally the superiority of pattern matching for L1 detection over alternative methods and discuss some of its potential applications. Results Pattern matching detected full-length L1 copies with high precision, reasonable computational time, and no prior input information. It also detected truncated and significantly altered copies of L1 with relatively high precision. The method was effectively used to annotate L1s in a target genome and to calculate copy number variation with respect to a reference genome. Crucial to the success of implementation was the selection of a small set of k-mer probes from a set of sequences presenting a stable pattern of distribution in the genome. As in seed-and-extend methods, the pattern matching algorithm sowed these k-mer probes, but instead of using heuristic extensions around the seeds, the analysis was based on distribution patterns within the genome. The desired level of precision could be adjusted, with some loss of recall. Conclusion Pattern matching is more efficient than seed-and-extend methods for the detection of L1 segments whose characterization depends on a finite set of sequences with common areas of low variability. We propose that pattern matching may help establish correlations between L1 copy number and disease states associated with L1 mobilization and evolution.

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Section: Methods and Resultsmentioning

confidence: 99%

Pattern matching for high precision detection of LINE-1s in human genomes

et al. 2022

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“…The format of the lexer is not standardized and varies between text editors. PyMOL lexers are available for several text editors 16 . If a PyMOL lexer is unavailable, the Python lexer works modestly well with the pymolsnips library.…”

Section: Discussionmentioning

confidence: 99%

Templates for writing PyMOL scripts

Mooers

Brown

2020

Protein Science

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PyMOL commands are used to exert exquisite control over the appearance of a molecular model. This control has made PyMOL popular for making images of protein structures for publications and presentations. However, many users have poor recall of the commands due to infrequent use of PyMOL. This poor recall hinders the writing of new code in scripts. One solution is to build the new script by using code fragments as templates for modular parts of the task at hand. The code fragments can be accessed from a library while writing the code from inside a text editor (e.g., Visual Studio Code, Vim, and Emacs). We developed a library of PyMOL code templates or snippets called pymolsnips to ease the writing of PyMOL code in scripts. We made pymolsnips available on GitHub in formats for 18 popular text editors. Most of the supported text editors are available for Mac, Windows, and Linux operating systems. The GitHub site includes animations that complement the instructions for installing the library for each text editor. We expect that the library will help many PyMOL users to be more productive when writing PyMOL script files.

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“…The software currently provides support for Clustal Omega 1.2.0 [24], MUSCLE 3.8.31 [25], MAFFT 7.312 [26], and T-Coffee 11 [27]; the user may use whichever they prefer, as long as it is already installed on their system. This was one of the first improvements made to L1PD, since previously the sequences had been manually aligned with the aid of bioSyntax [28]. It should be noted that bioSyntax has continued to be an incredibly useful tool for viewing files on a remote server.…”

Section: Probe Generationmentioning

confidence: 99%

Improved LINE-1 Detection through Pattern Matching by Increasing Probe Length

López,

Quiñones,

Martínez

2024

Biology

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Long Interspersed Element-1 (LINE-1 or L1) is an autonomous transposable element that accounts for 17% of the human genome. Strong correlations between abnormal L1 expression and diseases, particularly cancer, have been documented by numerous studies. L1PD (LINE-1 Pattern Detection) had been previously created to detect L1s by using a fixed pre-determined set of 50-mer probes and a pattern-matching algorithm. L1PD uses a novel seed-and-pattern-match strategy as opposed to the well-known seed-and-extend strategy employed by other tools. This study discusses an improved version of L1PD that shows how increasing the size of the k-mer probes from 50 to 75 or to 100 yields better results, as evidenced by experiments showing higher precision and recall when compared to the 50-mers. The probe-generation process was updated and the corresponding software is now shared so that users may generate probes for other reference genomes (with certain limitations). Additionally, L1PD was applied to other non-human genomes, such as dogs, horses, and cows, to further validate the pattern-matching strategy. The improved version of L1PD proves to be an efficient and promising approach for L1 detection.

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bioSyntax: syntax highlighting for computational biology

Cited by 3 publications

References 39 publications

Pattern matching for high precision detection of LINE-1s in human genomes

Pattern matching for high precision detection of LINE-1s in human genomes

Templates for writing PyMOL scripts

Improved LINE-1 Detection through Pattern Matching by Increasing Probe Length

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