Search Results for "mer"

...CNG provides an unbiased neural network approach to assess the importance of positional features that were determined by EDCC. To sustain a high computational performance even for large datasets, the mostly in Python 3 written programs use k-mer based indexing, parallelization and a neural network approach for categorization. For further information please refer to the publication: doi.org/10.1371/journal.pone.0190421

...mitoMaker calls well known assemblers and algorithms, such as SOAPdenovo, MIRA and blast+ and parses their results providing easily readable outputs, such as FASTA, GENBANK, SEQUIN, PNG and others. General pipeline: 1-iterative De Novo assembly, with different k-mer values, trying to assemble a build that matches a target mitochondrial genome given. 2-searches for all mitochondrial gene features and circularization. 3-stores the best result found. 4-uses the best assembly as backbone for a reference based assembly, using MIRA and MITObim, trying to extend the mitogenome and close gaps. 5-annotates the best assembly, identifying the start and end position of each and every feature. 6-creates a folder with all the results (PNG, GENBANK, FASTA, SEQUIN, CAF, MAF and a stats logfile).

...After various attempts to build different mitochondrial genomes in the lab I studied, a general pipeline started to appear: assemble reads with MIRA or SOAPdenovo-Trans, look for a scaffold/contig that matches a closely-related species, check it to see if all expected genomic features are present (since mitochondria is well conserved), check to see if the assembly might have circularized (since it's a circular DNA). If a feature was missing, or it hasn't circularized, try and assemble again with different assembler parameters (mainly k-mer). Rinse and repeat until the best build is found.