Aprenda Introduction to NGS Data | NGS Data Processing

Next-generation sequencing (NGS) technologies have transformed biological research by enabling rapid and cost-effective sequencing of DNA and RNA. NGS platforms, such as Illumina and Ion Torrent, generate vast quantities of sequence data in the form of short reads—typically ranging from 50 to 300 nucleotides in length. These reads can be produced as single-end (where only one end of the DNA fragment is sequenced) or paired-end (where both ends are sequenced, providing additional information about the fragment's orientation and insert size). The resulting data is commonly stored in files such as FASTQ, which encapsulate both the raw nucleotide sequences and their associated quality information.

# Example of a FASTQ file snippet
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAAT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**
# Line 1: Sequence identifier (@SEQ_ID)
# Line 2: Raw nucleotide sequence
# Line 3: Separator (+)
# Line 4: ASCII-encoded quality scores for each base

The quality of each read is a crucial aspect of NGS data analysis. Sequencing technologies are prone to various sources of error, including imperfect base calling, chemical noise, and instrument misreads. These errors can lead to incorrect identification of nucleotides, especially towards the ends of reads or in regions with homopolymers. To help address this, each base in a read is assigned a quality score, typically encoded as an ASCII character, which reflects the probability that the base was called incorrectly.


              1234567891011121314
            
def ascii_to_phred(qual_str):
    """
    Convert ASCII-encoded Phred quality scores to numerical values.
    Assumes Sanger encoding (Phred+33).
    """
    phred_scores = []
    for char in qual_str:
        phred_scores.append(ord(char) - 33)
    return phred_scores

# Example usage:
quality_line = "!''*((((***+))%%%++)(%%%%).1***-+*''))**"
phred_scores = ascii_to_phred(quality_line)
print(phred_scores)

These numerical quality scores are essential for downstream processing. Before using NGS reads for alignment or assembly, you often need to filter out low-quality reads or trim poor-quality bases from the ends. This step helps ensure that only high-confidence data is used in subsequent analyses, reducing spurious alignments and improving the reliability of biological conclusions.

1. What is the purpose of quality scores in NGS data?

2. Why might you need to trim reads before downstream analysis?

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What does a higher Phred quality score mean in terms of sequencing accuracy?

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# Example of a FASTQ file snippet
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAAT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**
# Line 1: Sequence identifier (@SEQ_ID)
# Line 2: Raw nucleotide sequence
# Line 3: Separator (+)
# Line 4: ASCII-encoded quality scores for each base


              1234567891011121314
            
def ascii_to_phred(qual_str):
    """
    Convert ASCII-encoded Phred quality scores to numerical values.
    Assumes Sanger encoding (Phred+33).
    """
    phred_scores = []
    for char in qual_str:
        phred_scores.append(ord(char) - 33)
    return phred_scores

# Example usage:
quality_line = "!''*((((***+))%%%++)(%%%%).1***-+*''))**"
phred_scores = ascii_to_phred(quality_line)
print(phred_scores)

1. What is the purpose of quality scores in NGS data?

2. Why might you need to trim reads before downstream analysis?

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 1