10 Next-Generation Sequencing Interview Questions and Answers

Next-Generation Sequencing (NGS) has revolutionized the field of genomics by enabling rapid and high-throughput sequencing of DNA and RNA. This technology has applications in various domains, including medical diagnostics, personalized medicine, evolutionary biology, and agricultural research. NGS offers unparalleled accuracy and depth, making it a critical tool for researchers and professionals in the life sciences.

This article provides a curated selection of interview questions designed to test your understanding and expertise in NGS. By reviewing these questions and their detailed answers, you will be better prepared to demonstrate your knowledge and problem-solving abilities in this cutting-edge field.

Next-Generation Sequencing Interview Questions and Answers

1. What are the key differences between Illumina, PacBio, and Oxford Nanopore sequencing platforms?

The key differences between Illumina, PacBio, and Oxford Nanopore sequencing platforms can be summarized as follows:

Illumina Sequencing:

• Read Length: Short reads, typically 150-300 base pairs.
• Accuracy: High accuracy with error rates around 0.1%.
• Throughput: High throughput, suitable for large-scale projects.
• Cost: Generally lower cost per base compared to long-read technologies.

PacBio Sequencing:

• Read Length: Long reads, averaging 10,000-15,000 base pairs, with some reads exceeding 50,000 base pairs.
• Accuracy: Moderate accuracy with higher error rates initially, but can be improved using circular consensus sequencing (CCS).
• Throughput: Lower throughput compared to Illumina, but sufficient for many applications.
• Cost: Higher cost per base due to longer reads and specialized equipment.

Oxford Nanopore Sequencing:

• Read Length: Ultra-long reads, potentially exceeding 1 million base pairs.
• Accuracy: Variable accuracy, with error rates around 5-15%, but improving with advancements in technology and software.
• Throughput: Flexible throughput, with portable devices available for fieldwork and larger devices for high-throughput sequencing.
• Cost: Variable cost, with lower initial investment for portable devices but higher per-base cost compared to Illumina.

2. Describe the common data formats used in NGS, such as FASTQ, BAM, and VCF.

Next-Generation Sequencing (NGS) generates large volumes of data, and several common data formats are used to store and manage this information. The most widely used formats are FASTQ, BAM, and VCF.

FASTQ is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores. Each entry in a FASTQ file consists of four lines: a sequence identifier, the raw sequence letters, a plus sign, and a line with quality scores.

BAM (Binary Alignment/Map) is a binary format for storing sequence data that has been aligned to a reference genome. BAM files are compressed versions of SAM (Sequence Alignment/Map) files and are used to store large amounts of alignment data efficiently. They support random access, which allows for quick retrieval of data from specific regions of the genome.

VCF (Variant Call Format) is a text file format used for storing gene sequence variations. VCF files contain meta-information lines, a header line, and data lines. Each data line represents a position in the genome and includes information about the reference and alternate alleles, quality scores, and additional annotations.

3. What are Q-scores, and why are they important in NGS quality control?

Q-scores, or quality scores, are numerical values that represent the probability of an incorrect base call in DNA sequencing. They are logarithmically related to the error probability, typically calculated using the formula:

Q = -10 * log10(P)

where P is the probability of an incorrect base call. For example, a Q-score of 20 corresponds to a 1 in 100 chance of an error, while a Q-score of 30 corresponds to a 1 in 1000 chance of an error.

In NGS, Q-scores are important for several reasons:

• Accuracy: High Q-scores indicate high confidence in the base calls, which is important for downstream analyses such as variant calling, genome assembly, and gene expression profiling.
• Filtering: Q-scores can be used to filter out low-quality reads, improving the overall quality of the dataset.
• Comparison: Q-scores allow for the comparison of sequencing quality across different runs, platforms, and protocols.
• Cost-effectiveness: By ensuring high-quality data, researchers can avoid the costs associated with re-sequencing or erroneous conclusions.

4. Compare and contrast the BWA and Bowtie sequence alignment algorithms.

BWA (Burrows-Wheeler Aligner) and Bowtie are both widely used sequence alignment algorithms in the field of next-generation sequencing (NGS). They are designed to align short DNA sequences (reads) to a reference genome, but they have different strengths and use cases.

• BWA is known for its accuracy and ability to handle longer reads. It uses the Burrows-Wheeler Transform (BWT) and the FM-index to efficiently map reads to the reference genome.
• BWA is particularly effective for aligning reads from high-throughput sequencing technologies like Illumina.
• It supports gapped alignments, which makes it suitable for aligning reads with insertions and deletions (indels).
• BWA has different modes, such as BWA-MEM, BWA-SW, and BWA-ALN, each optimized for different read lengths and types of sequencing data.

Bowtie:

• Bowtie is designed for ultra-fast alignment of short reads. It also uses the Burrows-Wheeler Transform and FM-index but is optimized for speed.
• Bowtie is particularly useful for applications where speed is critical, such as aligning large datasets quickly.
• It supports ungapped alignments and is less effective at handling indels compared to BWA.
• Bowtie has two versions: Bowtie and Bowtie2. Bowtie2 improves upon the original Bowtie by supporting gapped alignments and longer reads, making it more versatile.

5. How do you annotate genetic variants, and what tools/databases would you use?

Annotating genetic variants involves identifying and interpreting the functional consequences of genetic variations detected through next-generation sequencing (NGS). This process is important for understanding the potential impact of these variants on gene function and their association with diseases.

To annotate genetic variants, several tools and databases are commonly used:

• Ensembl Variant Effect Predictor (VEP): This tool predicts the functional effects of genetic variants on genes, transcripts, and protein sequences. It integrates data from various sources, including Ensembl, RefSeq, and UniProt.
• ANNOVAR: ANNOVAR is a versatile tool that annotates genetic variants by utilizing multiple databases, such as dbSNP, 1000 Genomes, and ClinVar. It provides information on variant frequency, functional impact, and disease associations.
• SnpEff: SnpEff is a tool that annotates and predicts the effects of genetic variants on genes and proteins. It supports multiple genomes and integrates data from various sources, including Ensembl and RefSeq.
• ClinVar: ClinVar is a public database that aggregates information about genomic variation and its relationship to human health. It provides clinical annotations for genetic variants, including pathogenicity and associated conditions.
• dbSNP: dbSNP is a database of single nucleotide polymorphisms (SNPs) and other types of genetic variation. It provides information on variant frequency, genomic location, and functional impact.

6. Discuss strategies for handling and managing large NGS datasets.

Handling and managing large Next-Generation Sequencing (NGS) datasets requires a combination of efficient data storage, processing, and analysis strategies. Here are some key strategies:

1. Data Storage Solutions:

• Cloud Storage: Utilizing cloud platforms like AWS, Google Cloud, or Azure can provide scalable storage solutions. These platforms offer services like Amazon S3, Google Cloud Storage, and Azure Blob Storage, which are designed to handle large datasets.
• Local Storage: High-performance local storage solutions, such as Network Attached Storage (NAS) or Storage Area Networks (SAN), can be used for faster data access and retrieval.

2. Data Processing Pipelines:

• Parallel Processing: Tools like Apache Hadoop and Apache Spark can be used to process large datasets in parallel, significantly reducing processing time.
• Workflow Management Systems: Systems like Nextflow, Snakemake, and Galaxy can help manage and automate complex data processing pipelines, ensuring reproducibility and scalability.

3. Data Compression:

• Lossless Compression: Techniques like gzip or bzip2 can be used to compress data without losing any information, which is important for maintaining data integrity.
• Specialized Formats: Using specialized file formats like CRAM for sequence data can reduce storage requirements while preserving essential information.

4. Data Analysis:

• High-Performance Computing (HPC): Leveraging HPC clusters can provide the computational power needed to analyze large NGS datasets efficiently.
• Bioinformatics Tools: Utilizing bioinformatics tools and software, such as BWA for alignment, GATK for variant calling, and SAMtools for data manipulation, can streamline the analysis process.

5. Data Management:

• Metadata Management: Properly managing metadata is important for tracking data provenance, ensuring data quality, and facilitating data sharing.
• Data Sharing and Collaboration: Platforms like NCBI’s Sequence Read Archive (SRA) and EMBL-EBI’s European Nucleotide Archive (ENA) can be used for data sharing and collaboration within the scientific community.

7. Develop a custom pipeline for RNA-Seq data analysis, including quality control, alignment, and differential expression analysis.

To develop a custom pipeline for RNA-Seq data analysis, you need to consider several key steps: quality control, alignment, and differential expression analysis. Each of these steps involves specific tools and methodologies to ensure accurate and reliable results.

1. Quality Control (QC):

• Before any analysis, it is important to assess the quality of the raw RNA-Seq data. Tools like FastQC can be used to generate quality reports, identifying issues such as low-quality reads, adapter contamination, or GC content biases.
• Trimming tools like Trimmomatic or Cutadapt can be employed to remove low-quality bases and adapter sequences, improving the overall quality of the data.

2. Alignment:

• The next step is to align the high-quality reads to a reference genome or transcriptome. Tools like STAR, HISAT2, or Bowtie2 are commonly used for this purpose. These aligners are designed to handle the large volumes of data generated by RNA-Seq experiments and can efficiently map reads to the reference.
• Post-alignment, tools like SAMtools can be used to convert, sort, and index the alignment files (BAM files), preparing them for downstream analysis.

3. Differential Expression Analysis:

• Once the reads are aligned, the next step is to quantify gene expression levels. Tools like featureCounts or HTSeq can be used to count the number of reads mapping to each gene.
• For differential expression analysis, tools like DESeq2, edgeR, or limma can be employed. These tools use statistical models to identify genes that are differentially expressed between conditions, taking into account factors such as biological variability and sequencing depth.

8. How would you troubleshoot an issue where a significant portion of your reads have low quality scores?

When troubleshooting low-quality scores in Next-Generation Sequencing (NGS) reads, it is important to consider several factors that could contribute to this issue. These factors can be broadly categorized into sample preparation, sequencing process, and data analysis.

1. Sample Preparation:

• Ensure that the DNA/RNA extraction process is optimized and free from contaminants.
• Verify the quality and quantity of the input material using methods such as Qubit or Bioanalyzer.
• Check for any degradation or fragmentation of the sample, which can affect sequencing quality.

2. Sequencing Process:

• Review the sequencing run parameters and ensure they are set correctly.
• Monitor the performance of the sequencing instrument, including the flow cell and reagents.
• Check for any issues with the clustering process, as poor clustering can lead to low-quality reads.

3. Data Analysis:

• Use quality control tools such as FastQC to assess the quality of the raw reads.
• Trim low-quality bases and adapter sequences using tools like Trimmomatic or Cutadapt.
• Align the reads to a reference genome and evaluate the alignment metrics to identify any anomalies.

9. Describe how you would integrate NGS data with other types of biological data, such as proteomics or metabolomics.

Integrating Next-Generation Sequencing (NGS) data with other types of biological data, such as proteomics or metabolomics, involves several steps to ensure that the data from different sources can be effectively combined and analyzed. The goal is to create a holistic view of the biological system under study.

1. Data Preprocessing: Each type of data (NGS, proteomics, metabolomics) needs to be preprocessed to ensure quality and consistency. This includes steps like normalization, noise reduction, and handling missing values.

2. Data Alignment: Align the data to a common reference, such as a genome or a protein database. This step ensures that the data points from different sources correspond to the same biological entities.

3. Feature Extraction: Extract relevant features from each data type. For NGS data, this might include gene expression levels, while for proteomics, it could be protein abundance, and for metabolomics, metabolite concentrations.

4. Data Integration: Use statistical and computational methods to integrate the data. Common approaches include:

• Multivariate Analysis: Techniques like Principal Component Analysis (PCA) or Canonical Correlation Analysis (CCA) can be used to identify patterns and correlations between different data types.
• Network Analysis: Constructing biological networks that incorporate data from different sources to identify key interactions and pathways.
• Machine Learning: Applying machine learning algorithms to integrate and analyze the data, potentially uncovering new biological insights.

5. Validation and Interpretation: Validate the integrated data using biological knowledge and experimental validation. Interpret the results in the context of the biological question being addressed.

10. Can you discuss any recent advances in NGS technology and their implications for the field?

Recent advances in Next-Generation Sequencing (NGS) technology have significantly enhanced the capabilities and applications of genomic research. Some of the notable advancements include:

• Long-Read Sequencing: Technologies such as PacBio’s Single Molecule Real-Time (SMRT) sequencing and Oxford Nanopore sequencing have improved the ability to read longer DNA fragments. This reduces the complexity of genome assembly and improves the detection of structural variants.
• Single-Cell Sequencing: Advances in single-cell RNA sequencing (scRNA-seq) allow for the analysis of gene expression at the individual cell level. This has implications for understanding cellular heterogeneity, developmental biology, and disease mechanisms.
• High-Throughput Sequencing: The development of more efficient and cost-effective sequencing platforms, such as Illumina’s NovaSeq, has increased the throughput and reduced the cost of sequencing. This makes large-scale genomic studies more feasible.
• Epigenetic Sequencing: Techniques like bisulfite sequencing and ATAC-seq have advanced our understanding of epigenetic modifications and chromatin accessibility, providing insights into gene regulation and epigenetic inheritance.
• CRISPR-Based Sequencing: The integration of CRISPR technology with NGS has enabled targeted sequencing and genome editing, allowing for precise manipulation and analysis of specific genomic regions.