High-performance computing in the search for the Tree of Life

在整个生命树的上限中，系统发育本身就是一个令人着迷的来源。自从我们知道物种有共同的祖先以来，我们就一直在寻求发现物种之间的关系模式。这种关系模式通常被表示为一棵分叉树，其顶端代表现存物种，内部节点代表假想的祖先。直到20世纪末，由于形态所能提供的数据数量有限，以及解释上的问题，系统发育研究一直是推测的。20世纪的两项技术发展给这一领域带来了革命性的变化。其一是计算机的可获得性，允许用算法和统计方法取代系统发育重建中的主观推理。另一个是DNA序列的可用性，允许作为系统发育重建基础的数据的质量和数量的阶梯变化。计算机和DNA序列对系统发育重建产生了变革性的影响。然而，在经历了几十年的稳定之后，进入21世纪，DNA测序技术正在发生变化。这种大数据给计算发展史带来了压力。在20世纪90年代，一名博士生可能会对20个物种中的每一个物种的单个基因进行测序，这一见解将是新颖和令人震惊的。现在，这位学生可能会对这些相同物种的整个基因组进行测序，从而有更大的机会发现和确定。随着DNA测序技术的不断发展，在十年内，这样的学生将能够对数千个物种的整个基因组进行测序。最后，在对生命树做出了近两个世纪的智力贡献之后，这棵树的整体几乎在我们的掌握之中--至少就输入数据而言。瓶颈已经从DNA序列的可用性转移到能够从这些数据中提取系统发生关系的算法和软件。要找到基于DNA序列的最佳系统发育，理想情况下，人们应该评估所有可能的树拓扑结构。然而，拓扑的数量随着现存物种的数量而按因子增加，对于54个物种来说，可能存在的无根分支拓扑比宇宙中的原子更多。唯一的出路是结合高性能计算(HPC)和先进的算法方法。这将以有效和快速的方式搜索解决方案的范围，并将最大限度地发挥日益可用的大量计算资源的潜力。LVB是一款系统发育重建软件，使用了先进的模拟退火法启发式算法。这使得只需查看一小部分可能的树拓扑即可获得良好(真实)的结果。我们寻求：-在并行计算机系统上实现直播，包括所有现有的台式计算机和通常在研究机构、大学和工业中发现的较大的HPC系统；-将LVB与其他系统进化重建软件相结合，以便通过一系列可信的标准进一步完善通过LVB快速获得的初步结果；-使可以通过Web服务器运行LVB，该服务器将记录用户输入的各个方面以及结果的质量，以便软件可以根据这些知识进行调整，以使其更好；-将LVB应用于已公布的、具有挑战性的73,060个真核物种的数据集，以获得生命树这一主要分支的比较性能数据和最佳可能的系统发育。

Phylogeny, in the upper limit the entire Tree of Life, is a source of fascination in itself. Ever since we have known species share common ancestors, we have sought to discover the pattern of relationships among species. The pattern of relationships is typically represented as a bifurcating tree, whose tips represent extant species and internal nodes represent hypothetical ancestors. Until the late 20th Century, phylogenetic research was speculative, due to the limited quantity of data morphology can provide and problems of interpretation. Two technological developments in the 20th Century revolutionised the field. One was the availability of computers, allowing the replacement of subjective reasoning in phylogeny reconstruction with algorithmic and statistical approaches. The other was the availability of DNA sequence, allowing a step-change in the quality and quantity of data upon which to base phylogeny reconstruction.Computers and DNA sequence have had a transformative effect on phylogeny reconstruction. However, after decades of stability, in the 21st century DNA sequencing technology is changing. This Big Data is placing a strain on computational phylogeny. In the 1990s, a PhD student might sequence a single gene from each of 20 species, and the insights would be novel and astounding. Now, the student might sequence the entire genome of these same species, with enormously greater opportunities for discovery and certainty. With continuing developments in DNA sequencing technology, within a decade such a student will be able to sequence the entire genome of several thousands of species. Finally, after almost two centuries of intellectual contributions on the tree of life, the tree in its entirety is almost within our grasp - with respect to input data at least.The bottleneck has shifted from DNA sequence availability, to algorithms and software able to extract phylogenetic relationships from these data.To find the optimal phylogeny based on DNA sequences, ideally one would evaluate all possible tree topologies. However, the number of topologies increases factorially with the number of extant species, and for 54 species there are more possible unrooted bifurcating topologies than atoms in the universe. The only way forward is a combined high performance computing (HPC) and advanced algorithmic approach. This will search the range of solutions in an effective and rapid manner, and will maximise the potential of increasingly available large computing resources.LVB is a piece of software for phylogeny reconstruction, using the advanced simulated annealing heuristic. This allows a good (realistic) result to be obtained, by looking at only a small fraction of possible tree topologies. We seek to:- implement LVB on parallel computer systems, including all current desktop computers and larger HPC systems, typically found in research institutes, universities and industry;- integrate LVB with other phylogeny reconstruction software, so that initial results obtained rapidly by LVB may be further refined by a range of plausible criteria;- make LVB available to run through a Web server, which will record aspects of the user's input as well as the quality of results, so that in future the software can adapt in light of this knowledge to be even better;- apply LVB to a published, challenging data set of 73,060 eukaryotic species, to obtain comparative performance data and the best possible phylogeny for this major branch of the Tree of Life.