Image metadata can be both scary and useful Assume that you are a field botanist on an expedition and that you take photographs of every collection site that you visit. The metadata that is routinely stored as part of your photograph can be used to visualize your collection sites on a map. For example, the […]
Archiv der Kategorie 'bioinformatics'
Life can be so easy if you speak grep
Habla usted grep? In the phylogenetic analyses of a manuscript draft, I accidentally named a species with the specific epithet “violaceae” where it should have been “violacea”. I had consistently used the wrong epithet for years, and countless subdirectories of subdirectories of subdirectories now contain analysis files that include the incorrect name. How can this […]
Buena vista con mVISTA
For an upcoming publication, a doctoral student and I want to visualize the sequence variability among several plastid genomes via the tool mVISTA. This tool is often employed in plastid phylogenomic studies and generally simple to use. However, if a user wishes to input custom annotations, it can be quite tricky to generate the correct […]
Quick Illumina read statistics in Bash
Recently, a Master’s student of mine asked me to re-calculate some data statistics of an Illumina sequencing run. Among the desired statistics were (a) the total number of read bases (in bp), (b) the total number of reads, (c) the GC content (in %), and (d) the AT content (in %). Since the average file […]
Automatically renaming contigs of assembly results
The genome assembly process often generates FASTA-formatted contig files, in which the contigs have cryptic sequence names. By using specific Bash commands, one can automatically rename these contigs based on the name of the file they are contained in. If your contig file contains only a single contig: for i in *__contig.fasta; do VAR=${i%__contig.fasta*}; sed […]
Massive plastid genome sequencing coincides with incomplete annotations of the inverted repeats
High numbers do not equate with high quality Over the past 10 years, the number of complete plastid genome sequences available on NCBI GenBank has skyrocketed, especially for flowering plants. Whereas in December 2009, approximately 120 complete plastid genomes of flowering plants were present on GenBank, there are 5,838 complete plastid genomes of flowering plants available […]
Improved sorting of numbered DNA sequences
Keeping things orderly Like many other molecular phylogeneticists, I often work with massive numbers of FASTA-formatted DNA sequences. Occasionally, the names of these sequences are numbered in a simplistic fashion (i.e., 1,2,…,48,49), which has the unfortunate side-effect of messing up the intended order of the sequences when sorted numerically, as the sequences 10, 11, …, […]
On the issue of file formats during DNA sequence submissions to GenBank
Ramblings on an important topic A series of software tools exist that allow users to conduct submissions of DNA sequences to NCBI GenBank, but file conversion represents a recurring challenge for those submissions. Similar to DNA sequence submissions to ENA, GenBank provides a wide range of options to upload annotated DNA sequences in a custom […]
Extracting mapped R1 and R2 reads from SAM file
Extracting those that mapped Recently, I found myself in need of extracting only those reads from a sequence alignment map (SAM) file that actually mapped to the reference genome, while maintaining the separation into the paired-end read design. By using a combination of samtools, bedtools and awk, this can be done very efficiently: INFL=MySamples.sam STEM=${INF%.sam*} […]
Setting burn-in and combining posterior tree distributions using awk and sed
Efficiency on the UNIX shell I often find myself manually removing a set of phylogenetic trees from a posterior tree distribution in order to set a burn-in and then combining the post-burnin trees of the individual runs. This action can be done very efficiently using awk on a UNIX shell: inf1=Mrbayes_test.run1.t inf2=Mrbayes_test.run2.t tmpf1=${inf1%.t*}_postBurnin.tre tmpf2=${inf2%.t*}_postBurnin.tre outf=${inf1%.run1.t*}_combined_postBurnin.tre […]
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