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doi:10.1101/pdb.ip49
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INTRODUCTION

Three methods--maximum parsimony, distance, and maximum likelihood--are generally used to find the evolutionary tree or trees that best account for the observed variation in a group of sequences. Each of these methods uses a different type of analysis. Programs based on distance methods are commonly used in the molecular biology laboratory because they are straightforward and can be used with a large number of sequences. Maximum likelihood methods are more challenging and require a greater understanding of the evolutionary models on which they are based. Because they involve so many computational steps and because the number of steps increases dramatically with the number of sequences, maximum likelihood programs are limited to a smaller number of sequences. They can be implemented on a supercomputer in order to analyze a greater number of sequences. This article presents an overview for the researcher who has a set of related sequences and wants to analyze them to predict the best trees that depict the phylogenetic relationships among the sequences.

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Maximum parsimony, distance methods, and the maximum likelihood approach are explained in more detail in the following CSH Protocols articles: Maximum Parsimony Method for Phylogenetic Prediction , Distance Methods for Phylogenetic Prediction , and The Maximum Likelihood Approach for Phylogenetic Prediction (all this issue).

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The flowchart below (Fig. 1) describes the types of considerations that need to be made in choosing a phylogenetic prediction method, but is not intended as a strict guide. It can be useful to try at least two of these methods, which can add confidence to the resulting analysis if the same results are obtained. These methods may find that more than one tree meet the criterion chosen for being the most likely tree. The branching patterns in these trees may be compared to find which branches are shared and therefore are more strongly supported. Phylogenetic analysis using parsimony (PAUP) provides methods for finding consensus trees, and such trees are also calculated by the CONSENSE program in the phylogenetic inference package (PHYLIP). Trees are stored as a tree file that shows the relationships in nested-parentheses notation. Sometimes, branch lengths are also included next to the names; e.g., A:0.05. From this information, a tree-drawing program may be used to produce a tree representation of the data.

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Does marijuana cause addiction? As Californians prepare to vote on Prop 19 — which would legalize recreational use of the drug, at least under state law — the question is more pertinent than ever. The answer, however, is less than clear: addiction experts tend to agree that pot is addictive, but nonspecialists and much of the public see it differently. It all depends on what you mean by “addiction.”

The definition most commonly accepted by addiction experts is a boiled-down version of the one laid out in the Diagnostic and Statistical Manual of Mental Disorders ( DSM-IV TR ), psychiatry’s handbook of all mental conditions. By the book, addiction is the compulsive use of a substance despite ongoing negative consequences, which may lead to tolerance or withdrawal symptoms when the substance is stopped. By this definition, about 10% of people who smoke marijuana become addicted to it. ( More on Time.com: PHOTOS — Cannabis Conventions ).

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However, nonspecialists (including many doctors) still tend to use an older perspective, now seen as outdated by experts. From their point of view, some drugs may be considered physically addictive — producing severe withdrawal — while others are psychologically addictive and only cause craving; those that are both are the hardest to quit.

Former Surgeon General Jocelyn Elders characterized marijuana succinctly on CNN recently , while declaring her support for legalization: “Marijuana is not addictive, not physically addictive anyway.”

In this view, the paradigm for addiction is heroin: the shaking, puking heroin junkie who can’t quit because the withdrawal sickness is impossible to bear. Because marijuana cessation is not linked with such severe symptoms, the drug isn’t seen as physically addictive. And considering that most people view physical addiction as uncontrollable, but psychological addiction as manageable with proper willpower, marijuana tends not to be regarded as addictive in general. ( More on Time.com: Addiction Files: Recovering From Drug Addiction,WithoutAbstinence ).

But virtually all addiction experts disagree with that stance. “The distinction is completely arbitrary. Psychological addiction occurs in your brain and it’s a physical change,” says Dr. Nora Volkow, director of the National Institute on Drug Abuse. Different brain processes may be involved in the psychological drive to take drugs and in the physical withdrawal symptoms when the drug is stopped — but both are brain changes.