Editing Sequence analysis (section)

== Gene prediction ==
{{Main|Gene prediction}}
Gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode [[genes]]. This includes protein-coding [[gene]]s as well as [[RNA gene]]s, but may also include the prediction of other functional elements such as [[regulatory regions]]. Geri is one of the first and most important steps in understanding the genome of a species once it has been [[Sequencing|sequenced]]. In general, the prediction of bacterial genes is significantly simpler and more accurate than the prediction of genes in eukaryotic species that usually have complex [[intron]]/[[exon]] patterns. Identifying genes in long sequences remains a problem, especially when the number of genes is unknown. [[Hidden markov models]] can be part of the solution.<ref>{{cite journal|last1=Stanke|first1=M|last2=Waack|first2=S|title=Gene prediction with a hidden Markov model and a new intron submodel|journal=Bioinformatics|volume=19 Suppl 2|date=Oct 19, 2003|issue=2|pages=215–25|pmid=14534192|doi=10.1093/bioinformatics/btg1080|doi-access=free}}</ref> Machine learning has played a significant role in predicting the sequence of transcription factors.<ref>{{cite journal|last1=Alipanahi|first1=B|last2=Delong|first2=A|last3=Weirauch|first3=MT|last4=Frey|first4=BJ|title=Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning|journal=Nat Biotechnol|date=Aug 2015|volume=33|issue=8|pages=831–8|doi=10.1038/nbt.3300|pmid=26213851|doi-access=free}}</ref> Traditional sequencing analysis focused on the statistical parameters of the nucleotide sequence itself (The most common programs used are listed in [https://www.ncbi.nlm.nih.gov/books/NBK20261/table/A181/?report=objectonly Table 4.1]). Another method is to identify homologous sequences based on other known gene sequences (Tools see [https://www.ncbi.nlm.nih.gov/books/NBK20261/table/A190/?report=objectonly Table 4.3]).<ref>{{cite journal|last1=Wooley|first1=JC|last2=Godzik|first2=A|last3=Friedberg|first3=I|title=A primer on metagenomics|journal=PLOS Comput Biol|date=Feb 26, 2010|volume=6|issue=2|doi=10.1371/journal.pcbi.1000667|pmid=20195499|pmc=2829047|pages=e1000667|bibcode=2010PLSCB...6E0667W |doi-access=free }}</ref>  The two methods described here are focused on the sequence. However, the shape feature of these molecules such as DNA and protein have also been studied and proposed to have an equivalent, if not higher, influence on the behaviors of these molecules.<ref>{{cite journal|last1=Abe|first1=N|last2=Dror|first2=I|last3=Yang|first3=L|last4=Slattery|first4=M|last5=Zhou|first5=T|last6=Bussemaker|first6=HJ|last7=Rohs R|first7=R|last8=Mann|first8=RS|title=Deconvolving the recognition of DNA shape from sequence|journal=Cell|date=Apr 9, 2015|volume=161|issue=2|pages=307–18|doi=10.1016/j.cell.2015.02.008|pmid=25843630|pmc=4422406}}</ref>