Here is an illustration of this pipeline:

Address of the bookmark: https://github.com/BGI-Qingdao/SLR-superscaffolder

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

More at https://academic.oup.com/bib/advance-article-abstract/doi/10.1093/bib/bbab022/6146772

Address of the bookmark: https://github.com/huangs001/AlignGraph2

1. Merge re-sequenced FastQ files (cat)
2. Read QC (FastQC)
3. Adapter trimming (fastp)
4. Removal of host reads (Kraken 2; optional)
5. Variant calling
   1. Read alignment (Bowtie 2)
   2. Sort and index alignments (SAMtools)
   3. Primer sequence removal (iVar; amplicon data only)
   4. Duplicate read marking (picard; optional)
   5. Alignment-level QC (picard, SAMtools)
   6. Genome-wide and amplicon coverage QC plots (mosdepth)
   7. Choice of multiple variant calling and consensus sequence generation routes (iVar variants and consensus; default for amplicon data || BCFTools, BEDTools; default for metagenomics data)
      • Variant annotation (SnpEff, SnpSift)
      • Consensus assessment report (QUAST)
      • Lineage analysis (Pangolin)
      • Clade assignment, mutation calling and sequence quality checks (Nextclade)
      • Individual variant screenshots with annotation tracks (ASCIIGenome)
   8. Intersect variants across callers (BCFTools)
6. De novo assembly
   1. Primer trimming (Cutadapt; amplicon data only)
   2. Choice of multiple assembly tools (SPAdes || Unicycler || minia)
      • Blast to reference genome (blastn)
      • Contiguate assembly (ABACAS)
      • Assembly report (PlasmidID)
      • Assembly assessment report (QUAST)
7. Present QC and visualisation for raw read, alignment, assembly and variant calling results (MultiQC)

Bactopia was inspired by Staphopia, a workflow we (Tim Read and myself) released that is targeted towards Staphylococcus aureus genomes. Using what we learned from Staphopia and user feedback, Bactopia was developed from scratch with usability, portability, and speed in mind from the start.

Bactopia uses Nextflow to manage the workflow, allowing for support of many types of environments (e.g. cluster or cloud). Bactopia allows for the usage of many public datasets as well as your own datasets to further enhance the analysis of your sequencing. Bactopia only uses software packages available from Bioconda and Conda-Forge to make installation as simple as possible for all users.

To highlight the use of Bactopia and Bactopia Tools, we performed an analysis of 1,664 public Lactobacillus genomes, focusing on Lactobacillus crispatus, a species that is a common part of the human vaginal microbiome. The results from this analysis are published in mSystems under the title: Bactopia: a flexible pipeline for complete analysis of bacterial genomes

Address of the bookmark: https://bactopia.github.io/latest/

Like other indel callers, Scalpel works by performing de novo assembly of regions of interest, so that misalignment to the reference genome cannot obscure the presence of an insertion or deletion. Scalpel's innovation is to repeatedly check its assembly before comparing to the reference genome, to account for simple sequence repeats that are a regular source of error in indel calling. When Scalpel assembles an exon, it collects reads that map to that exon (including partial matches), splits them into k-mers, and creates a de Bruijn graph to span the exon; however, if it detects repeats in the map, it iteratively increases the size of the k-mers by one base until the repeats are eliminated. This ensures that the final assembly of the exon is highly accurate while minimizing compute time.

The Cold Spring Harbor team's validation of Scalpel, published over the weekend in Nature Methods, compares Scalpel's performance on a live whole exome against HaplotypeCaller and SOAPindel. The donor is an individual with serious neurological disorders, which may be linked to a high incidence of indels. One thousand indels from this individual's exome, called by one or more of the informatics pipelines, were selected for focused resequencing. This resequencing revealed a 77% true positive rate for Scalpel calls, dramatically better than the rates for either of the competing tools; Scalpel performed especially well with indels longer than five base pairs, a traditional weak point for indel callers.

Finally, the authors demonstrate Scalpel's use on a large set of genetic data from nearly 600 families who donated samples to the Simons Simplex Collection, a project of the Simons Foundation Autism Research Initiative. Scalpel found a very high enrichment for indels in children affected by autism, compared with their unaffected siblings, a pattern that persisted even after excluding common variants.

Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on GitHub.

The Picard toolkit is open-source under the MIT license and free for all uses.

Enjoy!

Address of the bookmark: http://broadinstitute.github.io/picard/

ORFfinder is a graphical analysis tool for finding open reading frames (ORFs). We’ve been working on a few updates, and we’d like to find out what you think about them. Read on to find out what you can do with the new ORFfinder.

Smart BLAST (https://ncbiinsights.ncbi.nlm.nih.gov/2015/07/29/smartblast/)

Select one or a group of ORFs and BLAST several databases at once, and use the newly developed SmartBLAST to verify protein names. Looking for the traditional results from BLAST? They’re there too.

GAEMR’s analysis philosophy is centered on contiguity, correctness, and completeness -- how many pieces in an assembly composed of, how well those pieces accurately represent the genome sequenced, and how much of that genome is represented by those pieces. By performing over twenty different analyses based on these principles, GAEMR gives a clear picture of the condition of a genome assembly. 

Address of the bookmark: https://www.broadinstitute.org/software/gaemr/

• Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.
• Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).
• Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.
• GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.
• Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.
• Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.
• Open Source: Prodigal source code is freely available under the General Public License.

Address of the bookmark: http://prodigal.ornl.gov/