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Result file should contain 14 columns which are given as follows CHROM,POS,ID,REF,ALT,ALLELEID,CLNHGVS,CLNSIG,CLNVC,ORIGIN,RS,G ene_ID,Gene_symbol,Consequence

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Assignment 2 - Processing VCF file

1. Download the vcf file from https://spliceatlas.s3.amazonaws.com/clinvar_20220227_10K.vcf

2. File contains 10,000 lines

3. This is a kind of tsv file (tab separated values)

4. Line containing single # is the header. This contains the column headers

5. File will contain the following 8 columns (tab separated).

a. CHROM

b. POS

c. ID

d. REF

e. ALT

f. QUAL

g. FILTER

h. INFO

6. 2 lines from file is given here as a sample

a. #CHROM POS ID REF ALT QUAL FILTER INFO

b. 1     861332 1019397 G      A      .      .

ALLELEID=1003021;CLNDISDB=MedGen:CN517202;CLNDN=not_provided;CL

NHGVS=NC_000001.10:g.861332G>A;CLNREVSTAT=criteria_provided,_single

_submitter;CLNSIG=Uncertain_significance;CLNVC=single_nucleotide_variant;C LNVCSO=SO:0001483;GENEINFO=SAMD11:148398;MC=SO:0001583|missens e_variant;ORIGIN=1;RS=1640863258

c.   1     865519 1125147 C      T      .      .

ALLELEID=1110865;CLNDISDB=MedGen:CN517202;CLNDN=not_provided;CL

NHGVS=NC_000001.10:g.865519C>T;CLNREVSTAT=criteria_provided,_single_ submitter;CLNSIG=Likely_benign;CLNVC=single_nucleotide_variant;CLNVCSO

=SO:0001483;GENEINFO=SAMD11:148398;MC=SO:0001627|intron_variant;OR IGIN=1

7. Produce a output/result file (filename_of_ur_choice.csv - comma separated values) containing 14 columns by processing the downloaded file

a. Result file should contain 14 columns which are given as follows CHROM,POS,ID,REF,ALT,ALLELEID,CLNHGVS,CLNSIG,CLNVC,ORIGIN,RS,G ene_ID,Gene_symbol,Consequence

b. CHROM,POS,ID,REF,ALT can be collected directly from the first 5 columns of downloaded vcf file

c. To collect remaining data please use the last/8th col (INFO)

i. 8th col values are separated by ‘;’

ii. 8th col will have Attribute=value;Attribute=value;Attribute=value; and so on

 

iii. ALLELEID,CLNHGVS,CLNSIG,CLNVC,ORIGIN,RS can be collected directly from attributes of 8th col

1. ALLELEID=

2. CLNHGVS=

3. CLNSIG=

4. CLNVC=

5. ORIGIN=

6. RS=

iv. Gene_ID & Gene_Symbol can be collected from GENEINFO attribute in 8th column

1. GENEINFO=Gene_Symbol:Gene_ID

v. Consequence can be collected from MC attribute of 8th col

1. MC=SO:SO_ID|Consequence

vi. If any of the attribute isn’t available put ‘-’ in the result file

d. Expected o/p for the first 2 lines are

i. 1,861332,1019397,G,A,1003021,NC_000001.10:g.861332G>A,Uncertain

_significance,single_nucleotide_variant,1,1640863258,148398,SAMD11, missense_variant

ii. 1,865519,1125147,C,T,1110865,NC_000001.10:g.865519C>T,Likely_beni

gn,single_nucleotide_variant,1,-,148398,SAMD11,intron_variant

 

8. From the .csv file created in the above step, count the different type of origin (10th col)

a. Values of Origin col have the following meaning.

i. 0 - unknown;

ii. 1 - germline;

iii. 2 - somatic;

iv. 4 - inherited;

v. 8 - paternal;

vi. 16 - maternal;

vii. 32 - de-novo;

viii. 64 - biparental;

ix. 128 - uniparental;

x. 256 - not-tested;

xi. 512 - tested-inconclusive;

b. If you get number other than listed above, classify under ‘Others’

c. Make a data structure (dictionary) that has Origin type as key and the count of them as value

i. Expected o/p

'Others' => 95,

'de-novo' => 38,

'inherited' => 32,

'somatic' => 18,

'maternal' => 19,

'paternal' => 14,

 

'germline' => 6648,

'uniparental' => 6,

'unknown' => 145

d. Make a data structure of the same as mentioned in ( c ) into a json object. Display the results as a table using HTML

 

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