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When an equivalence group contained more than 30 candidate probes, we used a custom Python downselection program to choose an optimal subset.
We used a custom python script to calculate the ortholog hit ratio.
We then used a custom Python code to extract the sequences from the GRCh37 assembly stored locally.
We used a custom python script to remove potentially redundant annotations (we picked the longest ORF for annotations with the same stop codon).
From the 'best_candidates.eclipsed_orfs_removed.pep' file, we used a custom Python script (choose_longest.py) to parse the fasta headers, and remove all putative ORFs but the longest per subcomponent.
To produce a nonredundant set of Oases mRNA sequences rather than include alternative splicing, we used a custom python script (https://code.google.com/p/oases-to-csv/downloads/list) to choose a single representative transcript based on coverage and sequence length.
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Finally, GC content versus length profiles were generated using a custom python script (https://github.com/aemann01/gcLenCorPlots).
We converted the SFS data to SFS count tables using a custom python script (sfs_extraction.py 47.
Reads for each parent and progeny were identified by their unique 12 bp barcode identifier and sorted into individual files using a custom python script.
Marker alleles were converted into genotype codes based on the possible CP population segregation types abxcd, efxeg, hkxhk, lmxll, and nnxnp as described in the JoinMap® v4.150 manual using a custom python script.
Interpro domain identifiers24 from [http://matrisome.org] were used to query PlanMine30,31 [http://planmine.mpi-cbg.de] dd_Smed_v6 with p < 0.1 using the python API intermine v1.11.0 and collated using a custom python script using python 3.6.4 with pandas v0.22.0 and numpy v1.14.0.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com