Add consensus sequence figure

assets/bibliography.bib

@@ -517,3 +517,48 @@
   url             = {https://doi.org/10.1093/bioinformatics/btg109},
   eprint          = {https://academic.oup.com/bioinformatics/article-pdf/19/8/999/642375/btg109.pdf},
 }
+
+@Article{Nagar2013,
+  author          = {Nagar, Anurag and Hahsler, Michael},
+  title           = {Fast discovery and visualization of conserved regions in
+                  DNA sequences using quasi-alignment},
+  journal         = {BMC Bioinformatics},
+  year            = 2013,
+  month           = {Sep},
+  day             = 13,
+  volume          = 14,
+  number          = 11,
+  pages           = {S2},
+  abstract        = {Next Generation Sequencing techniques are producing
+                  enormous amounts of biological sequence data and analysis
+                  becomes a major computational problem. Currently, most
+                  analysis, especially the identification of conserved regions,
+                  relies heavily on Multiple Sequence Alignment and its various
+                  heuristics such as progressive alignment, whose run time grows
+                  with the square of the number and the length of the aligned
+                  sequences and requires significant computational resources. In
+                  this work, we present a method to efficiently discover regions
+                  of high similarity across multiple sequences without
+                  performing expensive sequence alignment. The method is based
+                  on approximating edit distance between segments of sequences
+                  using p-mer frequency counts. Then, efficient high-throughput
+                  data stream clustering is used to group highly similar
+                  segments into so called quasi-alignments. Quasi-alignments
+                  have numerous applications such as identifying species and
+                  their taxonomic class from sequences, comparing sequences for
+                  similarities, and, as in this paper, discovering conserved
+                  regions across related sequences.},
+  issn            = {1471-2105},
+  doi             = {10.1186/1471-2105-14-S11-S2},
+  url             = {https://doi.org/10.1186/1471-2105-14-S11-S2}
+}
+
+@book{book:771224,
+  title           = {Artificial Intelligence: A Modern Approach},
+  author          = {Stuart Russell, Peter Norvig},
+  publisher       = {Prentice Hall},
+  isbn            = {0136042597, 9780136042594},
+  year            = 2010,
+  series          = {Prentice Hall Series in Artificial Intelligence},
+  edition         = {3rd}
+}