1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
3. Includes binning and bin checking, for retrieving individual genomes
4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

1. Co-assembly procedure with read mapping for estimation of the abundances of genes in each metagenome
2. Co-assembly of a large number of metagenomes via merging of individual metagenomes
3. Includes binning and bin checking, for retrieving individual genomes
4. The results are stored in a database, where they can be easily exported and shared, and can be inspected anywhere using a web interface.
5. Internal checks for the assembly and binning steps inform about the consistency of contigs and bins, allowing to spot potential chimeras.
6. Metatranscriptomic support via mapping of cDNA reads against reference metagenomes

Address of the bookmark: https://github.com/jtamames/SqueezeMeta

More at http://www.bioinf.uni-leipzig.de/Software/segemehl/

Manual http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf

Address of the bookmark: http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html

Calculate how much sequencing you need to hit a target depth of coverage (or vice versa).

Instructions: set the read length/configuration and genome size, then select what you want to calculate.

Written by Stephen Turner, based on the Lander-Waterman formula, inspired by a similar calculator written by James Hadfield. Coverage is calculated as C=LN/G and reads as N=CG/L where C = Coverage (X),L = Read length (bp), G = Haploid genome size (bp), and N = Number of reads. Source code on GitHub.

Address of the bookmark: http://apps.bioconnector.virginia.edu/covcalc/

Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

1. Prepare a reference sequence (ref.fasta). Better a bacterial genome.
2. Download maq, maq-data and maqview at the download page.
3. Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
4. Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:

   maq.pl demo ref.fasta calib-30.dat
   

   where calib-30.dat is contained in maq-data.
5. View the alignment:

   cd maqdemo/easyrun;
   maqindex -i -c consensus.cns all.map;
   maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

Address of the bookmark: http://maq.sourceforge.net

Know of any other open source software for PacBio data? Email us.

Software categories:

• De novo assembly
• Structural Variations Detection
• Reference-based alignment
• Consensus and variant calling
• RNA analysis
• Epigenetic base modifications and methylation
• Barcoding
• Genome Browsers
• Run QC
• Frameworks and APIs

Address of the bookmark: https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software

Address of the bookmark: http://www.genome.umd.edu/

The "Ifs" of NGS QC and Trimming

1. Ensures Data Integrity
   If you want to minimize errors in downstream analyses, QC and trimming remove low-quality reads and bases, ensuring high-confidence data. This step is essential for reliable variant calling, assembly, and other applications.

2. Removes Contaminants
   If adapter sequences or contaminants are present in the raw reads, trimming can eliminate them. This prevents issues like misalignment or incorrect biological interpretations, ensuring cleaner data for analysis.

3. Improves Mapping and Assembly
   If your goal is better alignment to a reference genome or improved de novo assembly, trimming low-quality bases and adapters is critical. High-quality reads map more efficiently and generate more accurate assemblies.

4. Reduces Computational Load
   If you want to save computational resources, trimming reduces the dataset size, which speeds up processing and analysis. Clean datasets mean less computational time spent on processing low-quality data.

5. Prepares for Standardized Analyses
   If your project involves multiple datasets, QC and trimming ensure uniformity across them. This standardization makes comparisons valid and reproducible, particularly in large collaborative studies.

The "Buts" of NGS QC and Trimming

1. Risk of Over-Trimming
   But excessive trimming can lead to the loss of informative sequences, reducing read depth and potentially discarding biologically relevant data. This is especially critical in studies with limited sequencing depth.

2. Bias Introduction
   But trimming algorithms might introduce biases, especially if they inadvertently remove sequences with specific biological patterns. This can skew results and compromise biological insights.

3. Loss of Context in Paired-End Reads
   But trimming one read in a pair more than the other can lead to loss of pairing information. This complicates downstream analyses that rely on paired-end data, such as structural variant detection.

4. Time and Resource Intensive
   But running QC and trimming for large datasets can be computationally expensive and time-consuming. As sequencing depth increases, preprocessing becomes a bottleneck in the analysis pipeline.

5. Variable Standards
   But the criteria for trimming (e.g., quality threshold, minimum read length) can vary between tools and datasets. This variability may affect reproducibility and comparability of results across studies.

Balancing the "Ifs" and "Buts"

To maximize the benefits of QC and trimming while mitigating the challenges, consider the following best practices:

• Use QC Tools Wisely: Start with tools like FastQC to identify quality issues in your raw data. Visualizing quality metrics helps tailor your trimming parameters.

• Choose Reliable Trimming Tools: Tools like Trimmomatic, Cutadapt, and BBduk offer adaptive and customizable trimming options. Select one that aligns with your dataset and project goals.

• Set Reasonable Parameters: Avoid over-trimming by setting quality thresholds and minimum read lengths that balance data retention and quality improvement.

• Test Downstream Effects: Validate the impact of QC and trimming on downstream analyses, such as alignment efficiency, variant calling accuracy, or assembly quality.

• Document Your Workflow: Maintain detailed records of the parameters and tools used for QC and trimming. This ensures reproducibility and enables better troubleshooting.

Conclusion

NGS quality control and trimming are essential steps to ensure reliable and accurate data for analysis. While the "ifs" highlight the clear benefits of these steps, the "buts" remind us of the potential pitfalls. By adopting best practices and carefully balancing these considerations, you can optimize your preprocessing workflow and unlock the full potential of your sequencing data.

• Assembly of nanopore reads using Canu.
• Polish canu contigs using racon (optional).
• Map a paired-end Illumina dataset onto the contigs obtained in the previous steps using BWA mem.
• Perform correction of contigs using pilon and the Illumina dataset.

Address of the bookmark: https://github.com/nanoporetech/ont-assembly-polish

Quickstart

http://nanopolish.readthedocs.io/en/latest/quickstart_consensus.html

Algorithms

http://simpsonlab.github.io/2017/06/30/nanopolish-v0.7.0/

Address of the bookmark: https://github.com/jts/nanopolish