More at https://jobs.sanger.ac.uk/vacancy/senior-bioinformatician-assembly-moore-aquatic-symbiosis-project-tree-of-life-458923.html

Take a look at the PRICE software from the DeRisi lab. Its meant to do something very similar. http://derisilab.ucsf.edu/index.php?page=software

You could also look at SSPACE (http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/sspacev12/), ATLAS tools (http://www.hgsc.bcm.tmc.edu/content/bcm-hgsc-software), and SCARPA (http://compbio.cs.toronto.edu/hapsembler/scarpa.html).

See the PAGIT protocol: http://www.sanger.ac.uk/resources/software/pagit/

In particular, take a look at the IMAGE tool: http://genomebiology.com/2010/11/4/R41

Also SOAPdenovo has ha function for scaffolding. Not sure about ABYSS

Here there is a useful explanation of several tools.

https://bioinformaticsonline.com/search?q=scaffolding&entity_type=object&entity_subtype=bookmarks&offset=0&search_type=entities

I could be wrong, but the above answers to your hypothetical scenario appear to miss the point that you aren't interested in assembling the full genome, just the 100 kb part you're interested in. I suggest the following algorithm:

1. Start with the initial assembly C0 of the contigs you have identified as overlapping your region of interest, and the set S of reads those contigs contain. Let C = C0.

2. Repeat:
a. Identify paired-end reads (not in C) for which one or both ends align within, or extending, contigs in C.
b. Identify unpaired reads that align extending these new paired-end reads.
c. Construct a new assembly C' from C and the new reads identified in (a) and (b).
d. Trim C' so it does not extend more than 100 kb to either end of C0. Set C = C'.
e. Let S' denote the reads that contribute to C'. If S' does not contain any reads not present in S, stop. Otherwise, Set S = S'.

3. If you don't have a complete assembly of the region of interest, generate an STS for each end of each contig, probe a library for clones including these STSes, subclone these clones into a paired-end sequencing vector, and generate paired-end reads for this library; then try steps (1) and (2) again, adding these new sequencing reads to what you had before.

4. If your average sequencing depth for the region of interest exceeds 25 or so without filling all gaps, it is likely that the remaining gaps represent sequences that are not getting cloned in your sequencing vectors. Try different sequencing vectors.

Address of the bookmark: http://www.isical.ac.in/~bioinfo_miu/FOGSAA.htm

The DADA2 Workflow on Big Data goes through workflow optimized to run on large datasets (10s of millions to billions of reads).

An ITS-specific version of the DADA2 workflow identifies and verifiably removes primers on both ends of each ITS read, a key step due to the variable length of the ITS region.

Short demonstrations of assigning taxonomy and assigning species to sequences.

Address of the bookmark: https://benjjneb.github.io/dada2/index.html

Address of the bookmark: http://ml.ssu.ac.kr/gSearch/index.html

Address of the bookmark: http://frodock.chaconlab.org/

We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator design that adopts modern FPGA (field-programmable gate array) architectures to further boost the performance of our algorithm.

More at https://github.com/CMU-SAFARI/Shouji

Address of the bookmark: https://github.com/CMU-SAFARI/Shouji

Address of the bookmark: https://github.com/Megagenomics/FastProNGS

Address of the bookmark: https://github.com/MGXlab/Jaeger