dnctree 1.0.1

Distance-based phylogeny inference using a randomised divide-and-conquer method

dnctree: Randomized divide and conquer algorithm for phylogenetic trees

This is a distance-based method, inspired by Neighbor-Joining, for inferring phylogenies. Its main feature is that it scales to very large input datasets by avoiding estimating a pairwise distance matrix.

The input is a multiple sequence alignment and the output is a tree in standard Newick format. Option --json-output wraps the Newick-formatted tree in a JSON file, with some additional data describing the computation.

The implementation is (currently) 100% pure Python, but you can handle very large datasets in reasonable time anyways. See the preprint for some examples!

Algorithms

There are currently two algorithm versions implemented in dnctree.

• The default algorithm, 'core tree', seems to be as accurate as Neighbor-Joining in our experiments so far, but scales much better than Neighbor-Joining. We have submitted a paper on this algorithm.
• The 'simple' algorithms (see option --simple) is much faster, but has much worse accuracy than the core tree algorithm. The simple algorithm is described in a biorXiv preprint.

Both algorithms use Divide-and-Conquer. Problem instances with fewer sequences than what is given by the "base-case size", 100 by default, are handled by Neighbor-Joining and larger instances are partitioned and handled recursively.

Input formats

The formats Fasta, Phylip, Clustal, Nexus, and Stockholm (Pfam) are currently accepted input formats. This is determined by what the BioPython package accepts.

Example usage

dnctree testdata/s83_L500.phylip
dnctree -f phylip testdata/s83_L500.phylip   # Making it very clear input is a Phylip file
dnctree --simple  testdata/s83_L500.phylip   # Using the faster "simple" algorithm
dnctree --base-case-size 10 testdata/s83_L500.phylip  # Divide and conquer on larger inputs

Examples with output:

$ dnctree testdata/s83_L500.phylip
((((((L26,L27),(L24,L25)),((L28,L29),(L30,L31))),(((L16,L17),(L18,L19)),((L22,L23),(L20,L21)))),((((L8,L9),(L10,L11)),((L12,L13),(L14,L15))),(((L2,L3),(L0,L1)),((L6,L7),(L4,L5))))),(((((L86,L87),(L84,L85)),((L80,L81),(L82,L83))),(((L94,L95),(L92,L93)),((L90,L91),(L88,L89)))),((((L74,L75),(L72,L73)),((L78,L79),(L76,L77))),(((L64,L65),(L66,L67)),((L68,L69),(L70,L71))))),(((((L44,L45),(L46,L47)),((L42,L43),(L40,L41))),(((L34,L35),(L32,L33)),((L38,L39),(L36,L37)))),((((L50,L51),(L48,L49)),((L54,L55),(L52,L53))),(((L58,L59),(L56,L57)),((L62,L63),(L60,L61))))));

$ dnctree --json-output --base-case-size 10 testdata/s83_L500.phylip
{
    "version": "dnctree 1.0",
    "tree": "((((((L45,L44),(L47,L46)),((L43,L42),(L40,L41))),((((L54,L55),(L52,L53)),((L50,L51),(L48,L49))),(((L58,L59),(L57,L56)),((L63,L62),(L61,L60))))),(((L35,L34),(L33,L32)),((L39,L38),(L36,L37)))),(((((L91,L90),(L89,L88)),((L94,L95),(L92,L93))),(((L81,L80),(L82,L83)),((L87,L86),(L84,L85)))),((((L78,L79),(L77,L76)),(((L69,L68),(L70,L71)),((L65,L64),(L66,L67)))),((L74,L75),(L72,L73)))),(((((L7,L6),(L4,L5)),((L3,L2),(L0,L1))),((((L21,L20),(L23,L22)),((L16,L17),(L19,L18))),(((L30,L31),(L29,L28)),((L26,L27),(L24,L25))))),(((L9,L8),(L10,L11)),((L12,L13),(L14,L15)))));",
    "infile": "testdata/s83_L500.phylip",
    "aligned": true,
    "base-case-size": 10,
    "distances-computed": 1711,
    "fraction-computed-distances": 0.375,
    "n-taxa": 96,
    "comment": "Computed 1711 distances for 96 taxa. A full distance matrix would contain 4560 pairs. Savings: 62.5 %",
    "model-name": "WAG",
    "description": "AA alignment",
    "msa-width": 500,
    "computing-time": 0.907991542
}

Credits

• Amy Lee Jalsenius developed and implemented the "core tree" algorithm which is now the default.
• Mazen Mardini added PaHMM code (see https://github.com/marbogusz/paHMM-Tree), enabling experiments.