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Structural models were made using automated and manual modelling routines in an iterative manner using DeepView v3.7 SP5) [ 83, 84] for alignment and realignment of sequences and energy minimization of models and Swiss Model [ 84] software and servers for threading of models.
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Results include the native FlowerPower subfamily-HMM based alignment and a realignment of the sequences using MUSCLE.
Open image in new window Fig. 5 Bone segment alignment following distraction and realignment, lateral (a) and AP (b).
For gap-candidates these are a single contig-to-genome alignment and a gap-realignment; for split-candidates they are two contig-to-genome alignments.
To confirm the identity of the ROCO domains of these Entamoeba sequences we performed iterative multiple sequence alignments, HMM construction, database searches and realignment, to assemble the data presented as Figure 4.
Our modifications included the usage of Muscle [ 43] for protein alignment and subsequent use of the realignment tool of Csurös et al. [ 44] to optimize the recognition of conserved intron positions.
Duplicates were removed using Picard (http://broadinstitute.github.io/picard/) and realignment was performed using the GATK alignment procedures from the Broad Institute (https://www.broadinstitute.org/gatk/).org/gatk/
In our analysis, we used the variant calls generated by the BWA alignments and GATK indel realignment procedure, similar to as previously reported [ 19].
Refinements included trimming, removal of truncated and other defective sequences, recruitment of additional sequences, and realignment as necessary to create representative seed alignments.
Following read alignment, local realignment was performed using GATK version 2.4 [ 8], and duplicates were then marked using Picard version 1.54 [ 9].
Contigs were then scaffolded using the FPR3757 reference genome and multiple alignment was performed using the progressive Mauve engine of Mauve [ 29] followed by visual inspection and realignment.
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