While MetaSim generates reads from many input genomes and uses sequence-context error models, it cannot be trained on real data and does not assign quality values to reads, limiting its potential applications.
Projection projects all l-mers from the input sequences onto many buckets by hashing and then derives the consensus sequences to select some valid buckets.
Since these are computationally hard to find, we propose a preprocessing step to mask out from the input sequences as many positions as possible that cannot belong to a word of length L that is part of an (L, d, r -repeat.
The supertree approach seeks to amalgamate the information from many input trees and therefore is a sequence-based method that is more democratic in terms of getting its information.
OligoArray output, besides microarray design, provides hints on the quality (e.g. degree of redundancy) of input sequences by declaring how many specific probes can be designed based on input sequences.
When multiple contigs/singletons match the same database sequence, LANE runner 2.0 performs Clustal Omega (Sievers and Higgins 2014) alignments to define the relative positions of the input sequences and assembles them into a "one-to-many consensus".
As many as two suboptimal solutions can be provided through the web on each set of input sequences to potentially predict alternative promoters.
The evolutionary process varies across sequence sites and many features related to this may one day be inferred by sophisticated alignment methods from the input sequences along with their alignment.
When several input sequences match with a single database sequence, we obtain a "one-to-many consensus".
Many EFSM based testing problems, such as mutation testing, fault diagnosis, and test derivation involve the derivation of input sequences that distinguish configurations of a given EFSM specification.
