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Alignment Recovery for General Integer Scoring
Setyorinia, Kuspriyantob, Widyantoro D.H.b, Pancoro A.b
a Telkom University, School of Computing, Bandung, Indonesia
b Institute Teknologi Bandung, School of Electrical Engineering Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Alignment recovery is the process of recovering optimal alignment results based on weighted computational scores. Alignment recovery in classical dynamic programming (DP) is obtained by backtracking a trace created in the computational scoring phase. Bit parallelism is a parallel computing technique done by increasing the size of the word processing unit. The actual value of the score in the DP matrix is replaced by the difference of the adjacent matrix cell values. This requires adjustment of the scoring computation and its alignment recovery mechanism, especially for integer weights. General integer scoring involves a set of integers as its weights in bit-parallelism score computation that are not yet accompanied by alignment recovery. This paper presents an algorithm to recover the final alignment results from interpreting the horizontal and vertical adjacent-cell bit parallelism score values. The results show that the algorithm produces the same match pair result as the classical backtracking DP algorithm with less gap insertion. The algorithm requires O(m) space and time.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Adjacent matrix,Bit parallelism,Integer weights,Optimal alignments,Parallel computing techniques,Recovery mechanisms,Space and time[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]backtracking,bit-parallelism,dynamic programming,general integer scoring,sequence alignment[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/BioMIC48413.2019.9034737[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]