feat/add week2 Rosalind solutions file

students/sofya-d/rosalind/week2_solutions.md

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+# 1. Let's Be Practical.
+
+```python
+seq = input()
+counts = collections.Counter(seq)
+print( counts['A'], counts['C'], counts['G'], counts['T'], end = " ")
+
+```
+# 4. GenBank Introduction.
+
+```python
+from Bio import Entrez
+
+def genbank(genus, dtstart, dtend):
+    Entrez.email = "sumepremos@gmail.ru"
+    term = F"{genus}[Organism] AND {dtstart}[Publication Date] : {dtend}[Publication Date]"
+    handle = Entrez.esearch(db = "nucleotide", term = term)
+    record = Entrez.read(handle)
+    return record["Count"]
+
+print(genbank("Sulfurospirillum", "2007/10/25", "2009/02/03"))
+```
+
+# 5. Data Formats.
+
+```python
+from Bio import Entrez
+from Bio import SeqIO
+
+IDs = input().split()
+
+Entrez.email = "sumepremos@gmail.ru"
+handle = Entrez.efetch(db = "nucleotide", id = IDs, rettype = "fasta")
+records = list(SeqIO.parse(handle, "fasta"))
+
+ans_i = 0
+record_len = len(records[0].seq)
+
+for i in range(len(records)):
+    if len(records[i].seq) < record_len:
+        record_len = len(records[i].seq)
+        ans_i = i
+
+ans_handle = Entrez.efetch(db = "nucleotide", id = IDs[ans_i], rettype = "fasta")
+ans_record = ans_handle.read()
+
+print(ans_record)
+```
+
+# 6. FASTQ format introduction.
+
+```python
+from Bio import SeqIO
+
+count = SeqIO.convert("rosalind_tfsq.txt", "fastq", "seq.fasta", "fasta")
+print(f"{count} records converted")
+
+# 7. Read Quality Distribution.
+
+from Bio import SeqIO
+
+with open('fastq_threshold.txt', 'r') as fastq_threshold, open('fastq.txt', 'w') as fastq:
+    lines = fastq_threshold.readlines()
+    threshold = int(lines[0])
+    del lines[0]
+    for line in lines:
+        fastq.write(line)
+
+count = 0
+for record in SeqIO.parse("fastq.txt", "fastq"):
+    phred = record.letter_annotations["phred_quality"]
+    if sum(phred) / len(phred) < threshold:
+        count += 1
+
+print(count)
+```
+# 7. Read Quality Distribution.
+
+```python
+from Bio import SeqIO
+
+with open('fastq_threshold.txt', 'r') as fastq_threshold, open('fastq.txt', 'w') as fastq:
+    lines = fastq_threshold.readlines()
+    threshold = int(lines[0])
+    del lines[0]
+    for line in lines:
+        fastq.write(line)
+
+count = 0
+for record in SeqIO.parse("fastq.txt", "fastq"):
+    phred = record.letter_annotations["phred_quality"]
+    if sum(phred) / len(phred) < threshold:
+        count += 1
+
+print(count)
+```
+
+# 8. Read Filtration by Quality.
+
+```python
+from Bio import SeqIO
+
+with open('fastq_threshold.txt', 'r') as fastq_threshold, open('fastq.txt', 'w') as fastq:
+    lines = fastq_threshold.readlines()
+    values = lines[0].split()
+    threshold = int(values[0])
+    percentage = int(values[1])
+    del lines[0]
+    for line in lines:
+        fastq.write(line)
+
+count = 0
+for record in SeqIO.parse("fastq.txt", "fastq"):
+    phred = record.letter_annotations["phred_quality"]
+    ok_scores = 0
+    for score in phred:
+        if score >= threshold:
+            ok_scores += 1
+    if ok_scores / len(phred) * 100 >= percentage:
+        count += 1
+
+print(count)
+```
+
+# 9. Base Quality Distribution.
+
+```python
+from Bio import SeqIO
+
+with open('fastq_threshold.txt', 'r') as fastq_threshold, open('fastq.txt', 'w') as fastq:
+    lines = fastq_threshold.readlines()
+    threshold = int(lines[0])
+    del lines[0]
+    for line in lines:
+        fastq.write(line)
+
+cyc_count = 0
+all_phred = []
+for record in SeqIO.parse("fastq.txt", "fastq"):
+    phred = record.letter_annotations["phred_quality"]
+    cyc_count += 1
+    if all_phred == []:
+        all_phred = phred
+    for i in range(len(phred)):
+        if cyc_count != 1:
+            all_phred[i] += phred[i]
+
+for i in range(len(all_phred)):
+    all_phred[i] = all_phred[i] / cyc_count
+
+pos_count = 0
+for score in all_phred:
+    if score < threshold:
+        pos_count += 1
+
+print(pos_count)
+```