MB5021 Bioinformatics

1. Employed steps which improves quality filtered reads compared to raw reads and its assessments on FastQC reports;

• Quality control and filtering of sequence reads are important steps during processing. however, it is often not easy to identify and figure out which reads need to be adjusted and which can be left out. In FastQC Phred score concept on the usage of important quality metrics.
• In assessing the quality, the FASTQ files are input and return as HTML report which can be presented graphically illustrate information about the different reads quality. The FASTQ toll is run on Inside DNA cloudbuster, using a special wrapper called sub. The wrapper contains computational resources which help in distributing workload and computations between the different nodes in the cloud.

2. Short read sequencing and errors occurrences due too poor quality bases observation

• Sequencing of shorter read sequencing is often dependent on the instrument used. Short instrument falls between 100-600 base pairs while long read sequencing varies between 10-15 kb.
• Short read sequencing often yields sequences with scaffold gaps due to the high presence of genome sequence, having repeated sequences and missing insertions.
• Raw digital DNA sequences often consist of assessed nucleotides referred to as base pares. Quality control depends on the probability of the base pairs to deviate from the indemnity of the corresponding nucleotide in the genome reference. Its quality depends on the quality signal used in the reception of the nucleotide. (Kalari et al., 2014 pp 224)

3. Suffix Array for the sequences below:
4. ATCGTCGGAT

0	1	2	3	4	5	6	7	8	9	10
A	T	C	G	T	C	G	G	A	T	$

 

0	10	$
1	8	A T $
2	0	A T C G T C G G A T $
3	5	C G  G A T $
4	2	C G T C G G A T $
5	7	G A T $
6	6	G G A T $
7	3	G T C G G A T $
8	9	T $
9	4	T C G G A T $
10	1	A T C G T C G G A T $

1. GGCACGTACA

0	1	2	3	4	5	6	7	8	9	10
G	G	C	A	C	G	T	A	C	A	$

 

0	10	$
1	9	A  $
2	7	A C A  $
3	3	A  C G T A C A $
4	8	C A $
5	4	C G T A C A $
6	2	C A C G T A C A $
7	5	G T A C A $
8	1	G C A C G T A C A  $
9	0	G G C A C G T A C A  $
10	6	T A C A   $

4. BWT for the following sequences.
5. a) ATCGTCGGAT
6. b) GGCACGTACA
7. Produce the BWT for the following sequences.
8. Downstream application sequence and alignments

• Alignment-free sequences have been influential in the application of whole-genome phylogeny in the classification of proteins, identifying horizontally transferable genes and the detection of recombinant sequence, making them useful for next-generation sequencing data processing analysis.

Challenges

• Challenges observed s that, alignment programs often assume homologous sequence which has more conserved sequence stretches, further during analysis, alignments often drops significantly between the critical points.

6. Consequences of variant predictor effect predictor and sequence consequences on the phenotype of an individual.

• Occurrence of sequence variants occur in the coding regions which have protein effect on the amino acids which affect protein function. The variants can be seen in somatic cells which include nucleotide polymorphism.

This consequences include;-

• Intront variant
• Upstream gene variant
• Intergenic variant
• Missense variant
• NMD transcript
• Non coding transcript
• Regulatory region

Impact of variants

• Variants can have an effect on the substitution of the amino acids which lead to premature development which stops the cordon leading to incomplete proteins. Further the amino acids which is synonymous to single nucleotide polymorphisms which have an effect on the phenotypes which is related to development of diseases and drug response effect.

References

Chaisson, M.J. and Tesler, G., 2012. Mapping single molecule sequencing reads using basic local alignment with successive refinement (BLASR): application and theory. BMC bioinformatics, 13(1), p.238.

Kalari, K.R., Nair, A.A., Bhavsar, J.D., O’Brien, D.R., Davila, J.I., Bockol, M.A., Nie, J., Tang, X., Baheti, S., Doughty, J.B. and Middha, S., 2014. MAP-RSeq: mayo analysis pipeline for RNA sequencing. BMC bioinformatics, 15(1), p.224.

Leggett, R.M., Ramirez-Gonzalez, R.H., Clavijo, B., Waite, D. and Davey, R.P., 2013. Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics. Frontiers in genetics, 4, p.288.

Patel, R.K. and Jain, M., 2012. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PloS one, 7(2), p.e30619.